Implement txmd5_predictor application for Sic Bo analysis - #26
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Added a new Python application `txmd5_predictor` that continuously polls the Sic Bo API (every 500ms) using `asyncio` and `aiohttp`. The application features: - Core API polling and history tracking. - An algorithmic analyzer implementing Markov chains, trend analysis, recent majority, and sum analysis. - Automatic backtesting on startup and dynamic weight optimization (`_simulate_and_optimize`) triggered on incorrect predictions. - Integration with AI models (OpenAI, Gemini, Anthropic) via `ai_clients.py`. - Hourly automated reporting to a Telegram bot (`bot.py`). - Cloud deployment configurations including a Dockerfile, Procfile, and render.yaml. Co-authored-by: Tiengbip6110 <241485693+Tiengbip6110@users.noreply.github.com>
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📝 WalkthroughWalkthroughA new TXMD5 prediction service is introduced with configuration and orchestration for a polling-based game analyzer that uses multiple prediction algorithms, ensemble weighting, AI provider integrations, and Telegram reporting capabilities. ChangesTXMD5 Prediction Service
Sequence DiagramsequenceDiagram
participant Scheduler
participant MainApp
participant API
participant Analyzer
participant AIClients
participant TelegramBot
Scheduler->>MainApp: Start application
MainApp->>TelegramBot: Send startup message
loop Poll Loop (concurrent)
MainApp->>API: Fetch game history
API-->>MainApp: Return session list
MainApp->>Analyzer: Process initial batch
alt New Session Detected
MainApp->>Analyzer: Get actual outcome
MainApp->>Analyzer: Compare with last prediction
alt Prediction Mismatch
MainApp->>Analyzer: Trigger simulation & optimize weights
MainApp->>AIClients: Fetch suggestions (non-blocking)
AIClients->>AIClients: Query OpenAI, Gemini, Anthropic in parallel
end
MainApp->>Analyzer: Add new session to history
MainApp->>Analyzer: Generate ensemble prediction
end
MainApp->>MainApp: Sleep until next poll
end
loop Hourly Reporting (concurrent)
MainApp->>Analyzer: Get optimization stats
MainApp->>TelegramBot: Format & send hourly report
TelegramBot-->>TelegramBot: Build HTML message
end
Estimated code review effort🎯 4 (Complex) | ⏱️ ~60 minutes Poem
🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing Touches📝 Generate docstrings
🧪 Generate unit tests (beta)
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Actionable comments posted: 11
🧹 Nitpick comments (3)
txmd5_predictor/analyzer.py (1)
162-190: ⚖️ Poor tradeoffAvoid mutating
self.historyinside_simulate_and_optimize.Swapping
self.historyto a temp slice and restoring it infinallyworks for the current single-threaded loop, but it's brittle: any future concurrent caller (e.g., anotherawaitdriving prediction while a backtest is running) will see a partial history. A simpler, safer approach is to pass the slice into the predictors (or a private_predict_*that accepts history explicitly) so the simulation is purely functional.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@txmd5_predictor/analyzer.py` around lines 162 - 190, The loop in _simulate_and_optimize mutates self.history by swapping in temp_history then restoring it in finally; instead refactor so predictors are called functionally with the slice: create overloads or private variants like _predict_markov(history), _predict_trend(history), _predict_recent_majority(history) and _predict_sum_analysis(history) (or add an optional history param to predict_markov, predict_trend, predict_recent_majority, predict_sum_analysis) and call them with temp_history; remove the self.history replacement and restoration, keep use of determine_outcome(self.history[i]['point']) (or compute actual_outcome from temp_history if appropriate), and update accuracy_stats as before so the simulation no longer mutates self.history.txmd5_predictor/ai_clients.py (1)
62-62: 💤 Low valueMove
import asyncioto module top.Both
get_gemini_suggestionandget_all_suggestionsimportasyncioinside the function body. Hoist the import to the top of the file for clarity and to avoid repeated lookup overhead.♻️ Proposed change
import os import json import logging +import asyncio from openai import AsyncOpenAI import google.generativeai as genai from anthropic import AsyncAnthropic @@ - prompt = self._build_prompt(history) - import asyncio - model = genai.GenerativeModel('gemini-1.5-flash') + prompt = self._build_prompt(history) + model = genai.GenerativeModel('gemini-1.5-flash') @@ - async def get_all_suggestions(self, history: list) -> dict: - import asyncio - results = await asyncio.gather( + async def get_all_suggestions(self, history: list) -> dict: + results = await asyncio.gather(Also applies to: 95-95
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@txmd5_predictor/ai_clients.py` at line 62, Move the local "import asyncio" statements out of the function bodies and place a single "import asyncio" at the top of the module; update the module so get_gemini_suggestion and get_all_suggestions no longer perform an inline import and instead use the module-level asyncio reference to avoid repeated lookups and clarify imports.txmd5_predictor/bot.py (1)
33-45: ⚡ Quick winAdd a request timeout to prevent indefinite blocking on Telegram API calls.
The
session.post()call has no timeout, so a hung Telegram endpoint could block indefinitely (defaulting to aiohttp's 5-minute timeout). Adding an explicit timeout ensures the request fails fast. The proposed change usingClientTimeout(total=10)is the correct approach — thetotalparameter covers the entire operation (connection establishment, request sending, and response reading).Note: While creating a fresh
ClientSessionper call defeats connection pooling, this is minor here sincesend_messageis called only at startup and hourly. However, the timeout should definitely be added for robustness.Proposed change
- try: - async with aiohttp.ClientSession() as session: - async with session.post(self.api_url, json=payload) as response: + try: + timeout = aiohttp.ClientTimeout(total=10) + async with aiohttp.ClientSession(timeout=timeout) as session: + async with session.post(self.api_url, json=payload) as response:🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@txmd5_predictor/bot.py` around lines 33 - 45, The send_message implementation opens an aiohttp.ClientSession and calls session.post without a timeout, which can block indefinitely; update send_message to create the session with an aiohttp.ClientTimeout (e.g., ClientTimeout(total=10)) and pass it when constructing ClientSession so the session.post call uses that timeout, keeping the existing response handling and exception logging in the send_message method and referencing the same self.api_url and payload variables.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@render.yaml`:
- Line 4: The blueprint uses the deprecated env: field; replace the env key with
runtime and set its value to python (i.e., change env: python to runtime:
python) so the Render Blueprint uses the current schema; locate the env entry in
the YAML (the existing env key) and update it to runtime to fix the deprecation.
In `@txmd5_predictor/.env.example`:
- Line 2: Replace the real-looking TELEGRAM_CHAT_ID value in .env.example and
remove/override the hardcoded default in bot.py (the default used around the
TELEGRAM_CHAT_ID lookup at the top of bot.py) with a clearly marked placeholder
like "YOUR_TELEGRAM_CHAT_ID" so deployments don't accidentally message a real
user; update the example to match the other placeholders and ensure the code
reads the env var without falling back to the numeric default.
In `@txmd5_predictor/ai_clients.py`:
- Around line 56-68: The get_gemini_suggestion method currently uses the
deprecated google-generativeai/GenAI model instantiation
(genai.GenerativeModel('gemini-pro')) and should be migrated to the new
google-genai client and a supported model (e.g., 'gemini-2.5-pro'); update
imports at the module top to import the google_genai client and asyncio, replace
the genai.GenerativeModel usage with the google-genai API call pattern (create a
client, call the appropriate generate/text output method with the prompt and
model name), and handle the response mapping to return the generated text while
preserving the existing try/except and logging behavior in
get_gemini_suggestion.
In `@txmd5_predictor/analyzer.py`:
- Around line 19-22: The docstring for add_history incorrectly shows the example
payload using 'result' which will cause a KeyError because the code and API use
the 'point' key; update the docstring in analyzer.py for the add_history method
(the session_data example) to use {'phien': 123, 'point': 14, 'dice1': 4,
'dice2': 4, 'dice3': 6} so it matches how session_data is accessed elsewhere
(e.g., in predictors that read session_data['point']).
In `@txmd5_predictor/bot.py`:
- Around line 14-20: The code currently treats '8308036418' as an acceptable
default chat_id in txmd5_predictor.bot by checking self.chat_id == '8308036418'
and logging an informational message; remove this hardcoded default check and
instead validate only for presence/format (e.g., non-empty and numeric) so a
missing TELEGRAM_CHAT_ID is flagged as required. Update the validation around
bot_token and chat_id (references: self.bot_token, self.chat_id,
logger.warning/logger.error) to log or raise an error when chat_id is
absent/invalid and avoid any special-casing of that specific numeric value;
ensure self.api_url construction remains unchanged once valid values are
present.
In `@txmd5_predictor/Dockerfile`:
- Around line 1-10: Update the Dockerfile to run as a non-root user and make
COPY robust to different build contexts: create and use a non-root user (e.g.,
add/create user and switch with USER after WORKDIR) so the container doesn't run
as root, and either change the COPY lines to copy from the subdirectory (e.g.,
COPY txmd5_predictor/requirements.txt ./ and COPY txmd5_predictor/ .) or add a
brief comment documenting that the expected build context is the
txmd5_predictor/ directory; ensure any file ownership/permissions are adjusted
for the non-root user so pip install and runtime can access files (refer to the
Dockerfile commands WORKDIR, COPY, RUN pip install, and CMD).
In `@txmd5_predictor/main.py`:
- Around line 64-99: The polling loop currently sleeps a fixed 0.5s and will
hammer the API during repeated failures; modify the loop around fetch_api_data
in the while True block to implement a failure backoff: track consecutive
failure counts when fetch_api_data returns falsy or raises (in the fetch
wrapper), increase the sleep delay exponentially up to a cap (e.g., start 0.5s,
double to a max like 30s), and reset the counter/delay when a successful
data_list is received (i.e., when latest_item is processed and
analyzer.add_history() is called); ensure you use asyncio.sleep(new_delay) and
keep existing logic that handles new sessions, predictions and triggering
analyzer._simulate_and_optimize() unchanged.
- Around line 52-53: The code currently disables TLS verification by creating
aiohttp.TCPConnector(ssl=False); restore certificate validation by removing the
insecure flag and creating the connector with default SSL settings (e.g., use
aiohttp.TCPConnector() or
aiohttp.TCPConnector(ssl=ssl.create_default_context())) before passing it into
aiohttp.ClientSession; update the connector variable usage in main.py where
TCPConnector and ClientSession are used so HTTPS calls validate certificates
again (optionally support a configurable custom CA if needed).
- Around line 38-49: The try/except around the async HTTP call is catching all
Exceptions; replace the bare except with targeted handlers: catch
aiohttp.ClientError for request/network issues, asyncio.TimeoutError for the
timeout, and json.JSONDecodeError (or ValueError) for response.json parsing
errors, logging each with logger.error or logger.warning including the exception
details; for any truly unexpected errors use logger.exception and re-raise or
return None as appropriate. Locate the async with session.get(API_URL,
headers=headers, timeout=10) block and the response.json() call to add these
specific except clauses and include the error object in the log messages (refer
to session.get, response.json, and logger).
- Line 86: The fire-and-forget call to
self.ai_clients.get_all_suggestions(self.analyzer.history[-20:]) should be
tracked and its exceptions observed: instead of creating an unreferenced
asyncio.create_task, add the task to the same tracking collection used for the
other tasks (the tracked tasks referenced around the polling loop where tasks
are stored and awaited), attach a done callback to log exceptions (and remove
the task from the tracking set when complete), and ensure the task is
awaited/cleaned up during shutdown; reference the call site
(self.ai_clients.get_all_suggestions) and the analyzer.history slice when
implementing this change.
In `@txmd5_predictor/requirements.txt`:
- Around line 1-5: Update requirements.txt to pin minimum-safe versions instead
of leaving packages unpinned: replace bare package lines for aiohttp,
python-dotenv, openai, google-generativeai, and anthropic with floor-pinned
constraints (e.g., aiohttp>=3.13.4, python-dotenv>=1.0.0, openai>=1.0.0,
google-generativeai>=0.3.0, anthropic>=0.4.0) to ensure reproducible,
security-patched installs; after updating, regenerate your lockfile or vendor
list and add a note to periodically review/upgrade these minimums.
---
Nitpick comments:
In `@txmd5_predictor/ai_clients.py`:
- Line 62: Move the local "import asyncio" statements out of the function bodies
and place a single "import asyncio" at the top of the module; update the module
so get_gemini_suggestion and get_all_suggestions no longer perform an inline
import and instead use the module-level asyncio reference to avoid repeated
lookups and clarify imports.
In `@txmd5_predictor/analyzer.py`:
- Around line 162-190: The loop in _simulate_and_optimize mutates self.history
by swapping in temp_history then restoring it in finally; instead refactor so
predictors are called functionally with the slice: create overloads or private
variants like _predict_markov(history), _predict_trend(history),
_predict_recent_majority(history) and _predict_sum_analysis(history) (or add an
optional history param to predict_markov, predict_trend,
predict_recent_majority, predict_sum_analysis) and call them with temp_history;
remove the self.history replacement and restoration, keep use of
determine_outcome(self.history[i]['point']) (or compute actual_outcome from
temp_history if appropriate), and update accuracy_stats as before so the
simulation no longer mutates self.history.
In `@txmd5_predictor/bot.py`:
- Around line 33-45: The send_message implementation opens an
aiohttp.ClientSession and calls session.post without a timeout, which can block
indefinitely; update send_message to create the session with an
aiohttp.ClientTimeout (e.g., ClientTimeout(total=10)) and pass it when
constructing ClientSession so the session.post call uses that timeout, keeping
the existing response handling and exception logging in the send_message method
and referencing the same self.api_url and payload variables.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
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Configuration used: defaults
Review profile: CHILL
Plan: Pro Plus
Run ID: 05613042-b904-49a4-84f9-5a86d496e469
📒 Files selected for processing (9)
render.yamltxmd5_predictor/.env.exampletxmd5_predictor/Dockerfiletxmd5_predictor/Procfiletxmd5_predictor/ai_clients.pytxmd5_predictor/analyzer.pytxmd5_predictor/bot.pytxmd5_predictor/main.pytxmd5_predictor/requirements.txt
| services: | ||
| - type: worker | ||
| name: txmd5-ai-predictor | ||
| env: python |
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🧩 Analysis chain
🌐 Web query:
Render Blueprint render.yaml runtime vs env field worker python 2026
💡 Result:
In Render Blueprints, runtime and envVars are different concepts: 1) render.yaml runtime (service runtime) - runtime is a required service field (for service types other than keyvalue/redis) and selects the service’s language/environment (for native runtimes, includes python). [1] - This runtime value cannot be modified after the service is created. [1] 2) render.yaml envVars / “env” (environment variables passed to the process) - Blueprints configure a service’s environment via envVars entries (and environment groups via envVarGroups). [2][3] - Render’s “Environment Variables and Secrets” docs describe environment variables as customizing runtime behavior and discuss using Blueprints to declare them in render.yaml. [3] - The Blueprint spec notes that the older env field is discouraged, and that the runtime field “replaces the env field” (i.e., env is not the runtime selector; runtime is controlled by runtime). [1] 3) Worker + Python specifics - For a background worker service, you use type: worker and set runtime: python; you also configure worker behavior with envVars. [4] Practical takeaway for your question (what to use / what changes when): - Use runtime: python to select the Python runtime for that worker/service (and recognize it’s not mutable after creation). [1][4] - Use envVars to set environment variables that your worker’s Python code reads at runtime (safe to change via Blueprint sync). [2][3] Citations: [1] [2] [3] [4]
Citations:
- 1: https://docs.render.com/blueprint-spec
- 2: https://docs.render.com/infrastructure-as-code
- 3: https://render.com/docs/configure-environment-variables
- 4: https://github.com/openai/skills/blob/main/skills/.curated/render-deploy/references/blueprint-spec.md
Replace deprecated env: field with runtime: python
Render's Blueprint schema has deprecated the env: field in favor of runtime: for selecting the service language. Update line 4 to runtime: python per the current Render Blueprint specification.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@render.yaml` at line 4, The blueprint uses the deprecated env: field; replace
the env key with runtime and set its value to python (i.e., change env: python
to runtime: python) so the Render Blueprint uses the current schema; locate the
env entry in the YAML (the existing env key) and update it to runtime to fix the
deprecation.
| @@ -0,0 +1,7 @@ | |||
| TELEGRAM_BOT_TOKEN=your_telegram_bot_token | |||
| TELEGRAM_CHAT_ID=8308036418 | |||
There was a problem hiding this comment.
Don't ship a real-looking Telegram chat ID as the example default.
TELEGRAM_CHAT_ID=8308036418 looks like an actual Telegram chat/user ID rather than a placeholder. This same value is also treated as a "default" in bot.py (Line 16), which means a misconfigured deployment with a valid bot token but no chat-id override could send messages to whoever owns that chat. Use a placeholder string consistent with the other entries.
🔒️ Proposed change
TELEGRAM_BOT_TOKEN=your_telegram_bot_token
-TELEGRAM_CHAT_ID=8308036418
+TELEGRAM_CHAT_ID=your_telegram_chat_id📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| TELEGRAM_CHAT_ID=8308036418 | |
| TELEGRAM_BOT_TOKEN=your_telegram_bot_token | |
| TELEGRAM_CHAT_ID=your_telegram_chat_id |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@txmd5_predictor/.env.example` at line 2, Replace the real-looking
TELEGRAM_CHAT_ID value in .env.example and remove/override the hardcoded default
in bot.py (the default used around the TELEGRAM_CHAT_ID lookup at the top of
bot.py) with a clearly marked placeholder like "YOUR_TELEGRAM_CHAT_ID" so
deployments don't accidentally message a real user; update the example to match
the other placeholders and ensure the code reads the env var without falling
back to the numeric default.
| async def get_gemini_suggestion(self, history: list) -> str: | ||
| if not self.gemini_configured: | ||
| return "" | ||
| try: | ||
| # We would use asyncio.to_thread if we want to run synchronous gemini call async | ||
| prompt = self._build_prompt(history) | ||
| import asyncio | ||
| model = genai.GenerativeModel('gemini-pro') | ||
| response = await asyncio.to_thread(model.generate_content, prompt) | ||
| return response.text | ||
| except Exception as e: | ||
| logger.error(f"Gemini error: {e}") | ||
| return "" |
There was a problem hiding this comment.
🧩 Analysis chain
🌐 Web query:
google generativeai supported gemini model names 2026 gemini-pro deprecation
💡 Result:
As of 2026-05-06, Google’s supported Gemini model “names” (model codes / model IDs) depend on which platform you’re using. 1) Gemini API (ai.google.dev) — model codes (with preview deprecation) - Current/recommended Gemini 3 Pro line: - gemini-3.1-pro-preview [1] - gemini-3.1-pro-preview-customtools [1] - Deprecation/shutdown you asked about: - gemini-3-pro-preview has been shut down on March 9, 2026; migrate to Gemini 3.1 Pro Preview. [2][3] 2) Vertex AI (Google Cloud) — model IDs (example GA models) - Vertex AI documentation lists “generally available Gemini models” including at least: - gemini-2.5-pro - gemini-2.5-flash - gemini-2.5-flash-lite [4] - Vertex AI also provides a model retirement/deprecation lifecycle with explicit retirement dates for specific model IDs (example IDs shown in their lifecycle table). [5] If you tell me whether you mean (a) Gemini API (ai.google.dev) or (b) Vertex AI (cloud.google.com/vertex-ai), I can produce a complete verbatim list of every supported model code/ID from the official “Models” page for that surface (Gemini API vs Vertex AI) rather than only the key entries and the deprecation item you asked about.
Citations:
- 1: https://ai.google.dev/gemini-api/docs/models/gemini-3.1-pro-preview
- 2: https://ai.google.dev/gemini-api/docs/models
- 3: https://ai.google.dev/gemini-api/docs/models/gemini-3-pro-preview?hl=zh-tw
- 4: https://cloud.google.com/vertex-ai/generative-ai/docs/models
- 5: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions
🌐 Web query:
google-generativeai library gemini-pro model support 2026
💡 Result:
The google-generativeai Python library (legacy/deprecated as of late 2025) supported the original Gemini Pro model (gemini-pro or gemini-1.0-pro), but that model was retired by April/May 2025.[1][2] The current official library is google-genai (pip install google-genai), which fully supports modern Gemini Pro models via the Gemini API and Vertex AI as of 2026.[3][4][5] There is no active "gemini-pro" model; current equivalents include gemini-2.5-pro (GA, discontinuation not before Oct 2026),[6] gemini-3-pro-preview (discontinued March 2026),[7][8] and gemini-3.1-pro-preview (public preview since Feb 2026).[9][10] Example usage with google-genai: from google import genai client = genai.Client(api_key='YOUR_API_KEY') response = client.models.generate_content(model='gemini-2.5-pro', contents='Hello!') print(response.text) All Gemini Pro models (2.5-pro, 3.1-pro-preview, etc.) are supported in google-genai v1.74.0 (April 2026).[3][11]
Citations:
- 1: https://cloud.google.com/vertex-ai/generative-ai/docs/deprecations/gemini-1.0-pro
- 2: https://pypi.org/project/google-generativeai/
- 3: https://pypi.org/project/google-genai/
- 4: https://github.com/googleapis/python-genai/
- 5: https://cloud.google.com/vertex-ai/generative-ai/docs/sdks/overview
- 6: https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-pro
- 7: https://ai.google.dev/gemini-api/docs/models
- 8: https://aifreeapi.com/en/posts/gemini-3-pro-preview-not-found
- 9: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/3-1-pro
- 10: https://ai.google.dev/gemini-api/docs/models/gemini-3.1-pro-preview
- 11: googleapis/python-genai@060f015
🌐 Web query:
gemini-pro deprecated when google generativeai
💡 Result:
The Gemini 1.0 Pro model (model IDs: gemini-1.0-pro-001, gemini-1.0-pro-002, gemini-1.0-pro-vision-001), commonly referred to as "gemini-pro" in the google-generativeai Python SDK and Gemini API, was retired on April 21, 2025.[1][2][3] As of today's date (2026-05-06), it is a retired model, no longer accessible via API calls, which return 404 errors.[1][2] Google recommended upgrading to gemini-2.0-flash.[1][2] Additionally, the original google-generativeai Python SDK (used for the Gemini Developer API) was deprecated with Gemini 2.0 and reached end-of-life on November 30, 2025—all support ended permanently.[4][5][3] Users must migrate to the new unified Google GenAI SDK.[4][6] Note: "gemini-pro" specifically points to the 1.0 version; newer models like gemini-1.5-pro or gemini-2.5-pro have later retirement dates (e.g., gemini-1.5-pro-002 on September 24, 2025).[1][7]
Citations:
- 1: https://cloud.google.com/vertex-ai/generative-ai/docs/deprecations/gemini-1.0-pro
- 2: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions
- 3: Final. google-gemini/deprecated-generative-ai-python#755
- 4: https://github.com/google-gemini/generative-ai-python
- 5: https://github-redirect.dependabot.com/google-gemini/deprecated-generative-ai-python
- 6: https://ai.google.dev/gemini-api/docs/migrate
- 7: https://ai.google.dev/gemini-api/docs/deprecations
gemini-pro is deprecated and the google-generativeai library is end-of-life; migrate to google-genai with a current model.
The gemini-pro model (Gemini 1.0 Pro) was retired on April 21, 2025, and returns 404 errors. More critically, the google-generativeai library itself reached end-of-life on November 30, 2025. The codebase must migrate to the official google-genai library and use a currently supported model such as gemini-2.5-pro (GA as of May 2026). The models suggested previously (gemini-1.5-*) have also been retired by this date.
Update the library import and rewrite the call to use the new API:
🛠 Required migration
Replace google-generativeai with google-genai and update model and API usage:
- import asyncio
- model = genai.GenerativeModel('gemini-pro')
- response = await asyncio.to_thread(model.generate_content, prompt)
+ from google import genai
+ client = genai.Client(api_key=os.getenv('GOOGLE_API_KEY'))
+ response = await asyncio.to_thread(client.models.generate_content, model='gemini-2.5-pro', contents=prompt)(Move import asyncio to module top.)
🧰 Tools
🪛 Ruff (0.15.12)
[warning] 66-66: Do not catch blind exception: Exception
(BLE001)
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@txmd5_predictor/ai_clients.py` around lines 56 - 68, The
get_gemini_suggestion method currently uses the deprecated
google-generativeai/GenAI model instantiation
(genai.GenerativeModel('gemini-pro')) and should be migrated to the new
google-genai client and a supported model (e.g., 'gemini-2.5-pro'); update
imports at the module top to import the google_genai client and asyncio, replace
the genai.GenerativeModel usage with the google-genai API call pattern (create a
client, call the appropriate generate/text output method with the prompt and
model name), and handle the response mapping to return the generated text while
preserving the existing try/except and logging behavior in
get_gemini_suggestion.
| def add_history(self, session_data: Dict[str, Any]): | ||
| """Adds a new session to the history.""" | ||
| # session_data example: {'phien': 123, 'result': 14, 'dice1': 4, 'dice2': 4, 'dice3': 6} | ||
| self.history.append(session_data) |
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🏁 Script executed:
#!/bin/bash
# Confirm the key passed to add_history / process_initial_batch and the API field name.
rg -nP -C3 '\badd_history\s*\('
rg -nP -C3 '\bprocess_initial_batch\s*\('
rg -nP -C3 "\b(point|result)\b\s*[:=]" --type=pyRepository: Tiengbip6110/Tool
Length of output: 2989
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# Check the exact lines in analyzer.py that access keys from session_data
sed -n '40,70p' txmd5_predictor/analyzer.py
sed -n '64,75p' txmd5_predictor/analyzer.py
sed -n '85,115p' txmd5_predictor/analyzer.py
sed -n '160,175p' txmd5_predictor/analyzer.pyRepository: Tiengbip6110/Tool
Length of output: 3440
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# Check what data structure is being sent from main.py to add_history
rg -nA 5 -B 5 "latest_item\s*=" txmd5_predictor/main.pyRepository: Tiengbip6110/Tool
Length of output: 604
🏁 Script executed:
# Search for all direct accesses to 'point' or 'result' keys on session_data or history items
rg -n "\['(point|result)'\]" txmd5_predictor/analyzer.pyRepository: Tiengbip6110/Tool
Length of output: 618
Docstring example uses 'result' but the code and API both use 'point'.
The example payload in the docstring (line 21) shows 'result': 14, but every predictor reads session_data['point'] (lines 44, 45, 48, 68, 92, 108, 165). The API response also provides the 'point' field (main.py line 73). If a developer follows the docstring example, their code will raise KeyError. Update the docstring to reflect the actual key: {'phien': 123, 'point': 14, 'dice1': 4, 'dice2': 4, 'dice3': 6}.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@txmd5_predictor/analyzer.py` around lines 19 - 22, The docstring for
add_history incorrectly shows the example payload using 'result' which will
cause a KeyError because the code and API use the 'point' key; update the
docstring in analyzer.py for the add_history method (the session_data example)
to use {'phien': 123, 'point': 14, 'dice1': 4, 'dice2': 4, 'dice3': 6} so it
matches how session_data is accessed elsewhere (e.g., in predictors that read
session_data['point']).
| if not self.bot_token or self.bot_token == 'your_telegram_bot_token': | ||
| logger.warning("Telegram Bot Token is not configured.") | ||
| if not self.chat_id or self.chat_id == '8308036418': | ||
| # It's okay if it's the default, but we should log it | ||
| logger.info(f"Using default Telegram Chat ID: {self.chat_id}") | ||
|
|
||
| self.api_url = f"https://api.telegram.org/bot{self.bot_token}/sendMessage" |
There was a problem hiding this comment.
Remove the hardcoded chat-ID default from token validation logic.
Treating '8308036418' as a "known default" embeds someone's real Telegram chat ID into the code path. If TELEGRAM_CHAT_ID is left unset and falls back to that value, real messages can be delivered to that chat. Validate purely on presence/format and let TELEGRAM_CHAT_ID be required.
🔒️ Proposed change
- if not self.bot_token or self.bot_token == 'your_telegram_bot_token':
+ if not self.bot_token or self.bot_token == 'your_telegram_bot_token':
logger.warning("Telegram Bot Token is not configured.")
- if not self.chat_id or self.chat_id == '8308036418':
- # It's okay if it's the default, but we should log it
- logger.info(f"Using default Telegram Chat ID: {self.chat_id}")
+ if not self.chat_id or self.chat_id == 'your_telegram_chat_id':
+ logger.warning("Telegram Chat ID is not configured.")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if not self.bot_token or self.bot_token == 'your_telegram_bot_token': | |
| logger.warning("Telegram Bot Token is not configured.") | |
| if not self.chat_id or self.chat_id == '8308036418': | |
| # It's okay if it's the default, but we should log it | |
| logger.info(f"Using default Telegram Chat ID: {self.chat_id}") | |
| self.api_url = f"https://api.telegram.org/bot{self.bot_token}/sendMessage" | |
| if not self.bot_token or self.bot_token == 'your_telegram_bot_token': | |
| logger.warning("Telegram Bot Token is not configured.") | |
| if not self.chat_id or self.chat_id == 'your_telegram_chat_id': | |
| logger.warning("Telegram Chat ID is not configured.") | |
| self.api_url = f"https://api.telegram.org/bot{self.bot_token}/sendMessage" |
🧰 Tools
🪛 Ruff (0.15.12)
[error] 14-14: Possible hardcoded password assigned to: "bot_token"
(S105)
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@txmd5_predictor/bot.py` around lines 14 - 20, The code currently treats
'8308036418' as an acceptable default chat_id in txmd5_predictor.bot by checking
self.chat_id == '8308036418' and logging an informational message; remove this
hardcoded default check and instead validate only for presence/format (e.g.,
non-empty and numeric) so a missing TELEGRAM_CHAT_ID is flagged as required.
Update the validation around bot_token and chat_id (references: self.bot_token,
self.chat_id, logger.warning/logger.error) to log or raise an error when chat_id
is absent/invalid and avoid any special-casing of that specific numeric value;
ensure self.api_url construction remains unchanged once valid values are
present.
| try: | ||
| async with session.get(API_URL, headers=headers, timeout=10) as response: | ||
| if response.status == 200: | ||
| data = await response.json() | ||
| # The api historical data is under 'list' | ||
| if 'list' in data and len(data['list']) > 0: | ||
| return data['list'] | ||
| else: | ||
| logger.warning(f"API returned status {response.status}") | ||
| except Exception as e: | ||
| logger.error(f"Error fetching API data: {e}") | ||
| return None |
There was a problem hiding this comment.
🧩 Analysis chain
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head -100 txmd5_predictor/main.pyRepository: Tiengbip6110/Tool
Length of output: 4248
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# Also check if aiohttp and asyncio are imported
rg -n "^import|^from" txmd5_predictor/main.py | head -20Repository: Tiengbip6110/Tool
Length of output: 278
🏁 Script executed:
# Check if there are any other network/HTTP related error handling patterns in the codebase
rg -n "aiohttp\.|asyncio\." txmd5_predictor/ -A 2 -B 2 | head -50Repository: Tiengbip6110/Tool
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rg -n "JSONDecodeError|ValueError" txmd5_predictor/ --type pyRepository: Tiengbip6110/Tool
Length of output: 43
🏁 Script executed:
# Check the actual aiohttp exception hierarchy by examining if there are other network error patterns
rg -n "except.*Error" txmd5_predictor/ --type py -B 2 -A 3Repository: Tiengbip6110/Tool
Length of output: 43
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# Check bot.py's exception handling for comparison
sed -n '33,40p' txmd5_predictor/bot.pyRepository: Tiengbip6110/Tool
Length of output: 459
Replace broad exception catch with specific exception handling to improve observability.
The bare except Exception at line 47 masks real defects and makes API failures harder to diagnose. Specific exception types should be handled separately from unexpected errors.
Suggested fix
try:
async with session.get(API_URL, headers=headers, timeout=10) as response:
if response.status == 200:
data = await response.json()
# The api historical data is under 'list'
if 'list' in data and len(data['list']) > 0:
return data['list']
else:
logger.warning(f"API returned status {response.status}")
- except Exception as e:
- logger.error(f"Error fetching API data: {e}")
+ except (aiohttp.ClientError, asyncio.TimeoutError, ValueError) as e:
+ logger.warning(f"API fetch failed: {e}")
+ except Exception:
+ logger.exception("Unexpected error in fetch_api_data")
return None📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| try: | |
| async with session.get(API_URL, headers=headers, timeout=10) as response: | |
| if response.status == 200: | |
| data = await response.json() | |
| # The api historical data is under 'list' | |
| if 'list' in data and len(data['list']) > 0: | |
| return data['list'] | |
| else: | |
| logger.warning(f"API returned status {response.status}") | |
| except Exception as e: | |
| logger.error(f"Error fetching API data: {e}") | |
| return None | |
| try: | |
| async with session.get(API_URL, headers=headers, timeout=10) as response: | |
| if response.status == 200: | |
| data = await response.json() | |
| # The api historical data is under 'list' | |
| if 'list' in data and len(data['list']) > 0: | |
| return data['list'] | |
| else: | |
| logger.warning(f"API returned status {response.status}") | |
| except (aiohttp.ClientError, asyncio.TimeoutError, ValueError) as e: | |
| logger.warning(f"API fetch failed: {e}") | |
| except Exception: | |
| logger.exception("Unexpected error in fetch_api_data") | |
| return None |
🧰 Tools
🪛 Ruff (0.15.12)
[warning] 47-47: Do not catch blind exception: Exception
(BLE001)
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@txmd5_predictor/main.py` around lines 38 - 49, The try/except around the
async HTTP call is catching all Exceptions; replace the bare except with
targeted handlers: catch aiohttp.ClientError for request/network issues,
asyncio.TimeoutError for the timeout, and json.JSONDecodeError (or ValueError)
for response.json parsing errors, logging each with logger.error or
logger.warning including the exception details; for any truly unexpected errors
use logger.exception and re-raise or return None as appropriate. Locate the
async with session.get(API_URL, headers=headers, timeout=10) block and the
response.json() call to add these specific except clauses and include the error
object in the log messages (refer to session.get, response.json, and logger).
| connector = aiohttp.TCPConnector(ssl=False) | ||
| async with aiohttp.ClientSession(connector=connector) as session: |
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🏁 Script executed:
# First, let's look at the context around lines 52-53 in the file
cat -n txmd5_predictor/main.py | sed -n '45,60p'Repository: Tiengbip6110/Tool
Length of output: 941
🏁 Script executed:
# Let's see the entire function to understand the context better
cat -n txmd5_predictor/main.py | head -100Repository: Tiengbip6110/Tool
Length of output: 4948
🌐 Web query:
aiohttp TCPConnector ssl=False certificate verification behavior
💡 Result:
When using aiohttp.TCPConnector(ssl=False), certificate verification is skipped for HTTPS connections. The official documentation explicitly states: ssl=False for skip SSL certificate validation [1][2]. Internally, if req.ssl is False (or not True), the connector returns an unverified SSLContext (_SSL_CONTEXT_UNVERIFIED) that disables certificate checks [3]. This allows connections to servers with invalid, self-signed, or expired certificates, though the TLS handshake still occurs using Python's ssl module with verification disabled. Note that older deprecated parameters like verify_ssl=False behave similarly, but ssl is the preferred method since version 3.0 [1]. In aiohttp 4.0 (dev), this behavior continues [1]. Example usage: connector = aiohttp.TCPConnector(ssl=False) async with aiohttp.ClientSession(connector=connector) as session: async with session.get('https://example.com') as resp:...
Citations:
- 1: https://docs.aiohttp.org/en/latest/client_reference.html?highlight=TCPConnector
- 2: https://aiohttp.readthedocs.io/en/stable/client_reference.html
- 3: http://aiohttp.readthedocs.io/en/stable/_modules/aiohttp/connector.html
Re-enable TLS certificate verification for API calls.
Line 52 disables HTTPS certificate validation with ssl=False, allowing potential MITM attacks on the API connection to https://wtxmd52.tele68.com.
Suggested fix
- connector = aiohttp.TCPConnector(ssl=False)
- async with aiohttp.ClientSession(connector=connector) as session:
+ async with aiohttp.ClientSession() as session:📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| connector = aiohttp.TCPConnector(ssl=False) | |
| async with aiohttp.ClientSession(connector=connector) as session: | |
| async with aiohttp.ClientSession() as session: |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@txmd5_predictor/main.py` around lines 52 - 53, The code currently disables
TLS verification by creating aiohttp.TCPConnector(ssl=False); restore
certificate validation by removing the insecure flag and creating the connector
with default SSL settings (e.g., use aiohttp.TCPConnector() or
aiohttp.TCPConnector(ssl=ssl.create_default_context())) before passing it into
aiohttp.ClientSession; update the connector variable usage in main.py where
TCPConnector and ClientSession are used so HTTPS calls validate certificates
again (optionally support a configurable custom CA if needed).
| while True: | ||
| data_list = await self.fetch_api_data(session) | ||
|
|
||
| if data_list: | ||
| latest_item = data_list[0] | ||
| current_phien = latest_item.get('id') | ||
|
|
||
| if current_phien != self.last_phien: | ||
| # New session detected | ||
| logger.info(f"New session detected: {current_phien} - Result: {latest_item.get('point')}") | ||
|
|
||
| # Verify previous prediction | ||
| actual_outcome = self.analyzer.determine_outcome(latest_item.get('point')) | ||
| if self.last_prediction: | ||
| if self.last_prediction == actual_outcome: | ||
| logger.info(f"Prediction CORRECT! Predicted: {self.last_prediction}") | ||
| else: | ||
| logger.info(f"Prediction WRONG! Predicted: {self.last_prediction}, Actual: {actual_outcome}") | ||
| logger.info("Triggering simulation and optimization due to error...") | ||
| self.analyzer._simulate_and_optimize() | ||
|
|
||
| # Optionally get AI suggestions on error (non-blocking) | ||
| asyncio.create_task(self.ai_clients.get_all_suggestions(self.analyzer.history[-20:])) | ||
|
|
||
| # Add to history | ||
| self.analyzer.add_history(latest_item) | ||
| self.last_phien = current_phien | ||
|
|
||
| # Make new prediction | ||
| pred, conf, logic = self.analyzer.ensemble_predict() | ||
| self.last_prediction = pred | ||
| self.last_prediction_confidence = conf | ||
|
|
||
| logger.info(f"Next Prediction: {pred} (Confidence: {conf*100:.1f}%) using {logic}") | ||
|
|
||
| await asyncio.sleep(0.5) |
There was a problem hiding this comment.
Add failure backoff in the 500ms poll loop to prevent hammering on outages.
The loop retries every 500ms even during repeated API failures. That can amplify upstream incidents and create noisy churn.
Suggested fix
logger.info("Starting 500ms continuous polling loop.")
+ consecutive_failures = 0
while True:
data_list = await self.fetch_api_data(session)
if data_list:
+ consecutive_failures = 0
latest_item = data_list[0]
current_phien = latest_item.get('id')
@@
- await asyncio.sleep(0.5)
+ else:
+ consecutive_failures += 1
+
+ backoff = min(10.0, 0.5 * (2 ** min(consecutive_failures, 4)))
+ await asyncio.sleep(backoff if consecutive_failures else 0.5)🧰 Tools
🪛 Ruff (0.15.12)
[warning] 86-86: Store a reference to the return value of asyncio.create_task
(RUF006)
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@txmd5_predictor/main.py` around lines 64 - 99, The polling loop currently
sleeps a fixed 0.5s and will hammer the API during repeated failures; modify the
loop around fetch_api_data in the while True block to implement a failure
backoff: track consecutive failure counts when fetch_api_data returns falsy or
raises (in the fetch wrapper), increase the sleep delay exponentially up to a
cap (e.g., start 0.5s, double to a max like 30s), and reset the counter/delay
when a successful data_list is received (i.e., when latest_item is processed and
analyzer.add_history() is called); ensure you use asyncio.sleep(new_delay) and
keep existing logic that handles new sessions, predictions and triggering
analyzer._simulate_and_optimize() unchanged.
| self.analyzer._simulate_and_optimize() | ||
|
|
||
| # Optionally get AI suggestions on error (non-blocking) | ||
| asyncio.create_task(self.ai_clients.get_all_suggestions(self.analyzer.history[-20:])) |
There was a problem hiding this comment.
🧩 Analysis chain
🏁 Script executed:
cat -n txmd5_predictor/main.py | head -100Repository: Tiengbip6110/Tool
Length of output: 4948
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cat -n txmd5_predictor/ai_clients.py | head -150Repository: Tiengbip6110/Tool
Length of output: 5378
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rg "asyncio.create_task|create_task" txmd5_predictor/Repository: Tiengbip6110/Tool
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Length of output: 1088
Track background AI tasks to prevent task accumulation and unobserved exceptions.
Line 86 creates a fire-and-forget task inside the polling loop. When prediction errors occur repeatedly, this can accumulate pending tasks without exception visibility. Unlike the properly-tracked tasks at lines 113–114 (stored and awaited), this one drops the reference entirely.
Consider tracking background tasks and logging exceptions:
Suggested fix
class MainApp:
def __init__(self):
self.analyzer = Txmd5Analyzer()
self.bot = TelegramBot()
self.ai_clients = AIClients()
+ self._ai_tasks = set()
self.last_phien = None
self.last_prediction = None
self.last_prediction_confidence = 0.0
@@
- asyncio.create_task(self.ai_clients.get_all_suggestions(self.analyzer.history[-20:]))
+ task = asyncio.create_task(
+ self.ai_clients.get_all_suggestions(self.analyzer.history[-20:])
+ )
+ self._ai_tasks.add(task)
+ task.add_done_callback(self._ai_tasks.discard)
+ task.add_done_callback(
+ lambda t: logger.warning(f"AI suggestion task failed: {t.exception()}")
+ if t.exception() else None
+ )🧰 Tools
🪛 Ruff (0.15.12)
[warning] 86-86: Store a reference to the return value of asyncio.create_task
(RUF006)
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@txmd5_predictor/main.py` at line 86, The fire-and-forget call to
self.ai_clients.get_all_suggestions(self.analyzer.history[-20:]) should be
tracked and its exceptions observed: instead of creating an unreferenced
asyncio.create_task, add the task to the same tracking collection used for the
other tasks (the tracked tasks referenced around the polling loop where tasks
are stored and awaited), attach a done callback to log exceptions (and remove
the task from the tracking set when complete), and ensure the task is
awaited/cleaned up during shutdown; reference the call site
(self.ai_clients.get_all_suggestions) and the analyzer.history slice when
implementing this change.
| aiohttp | ||
| python-dotenv | ||
| openai | ||
| google-generativeai | ||
| anthropic |
There was a problem hiding this comment.
🧩 Analysis chain
🌐 Web query:
What is the latest secure aiohttp version and minimum version that fixes the GHSA-2vrm-gr82-f7m5, GHSA-8495-4g3g-x7pr request smuggling CVEs?
💡 Result:
Latest secure aiohttp version (as of 2026-05-06): 3.13.5 [1]. Minimum aiohttp versions that fix the specific request-smuggling-related GHSA advisories: 1) GHSA-2vrm-gr82-f7m5 (CVE-2026-34514): patched in aiohttp 3.13.4; affected versions are <= 3.13.3 [2][3]. 2) GHSA-8495-4g3g-x7pr (CVE-2024-52304, request smuggling via chunk extension newline parsing): patched in aiohttp 3.10.11; affected versions are <= 3.10.10 [4][5]. Therefore, to remediate both, upgrade to at least aiohttp 3.13.4 (and ideally the latest 3.13.5). [2][4][1]
Citations:
- 1: https://pypi.org/project/aiohttp/
- 2: GHSA-2vrm-gr82-f7m5
- 3: https://nvd.nist.gov/vuln/detail/CVE-2026-34514
- 4: GHSA-8495-4g3g-x7pr
- 5: https://nvd.nist.gov/vuln/detail/CVE-2024-52304
Pin dependency versions with minimum-safe constraints.
All five dependencies are unpinned. For a worker deployed to Render/Docker, this means reproducibility drifts every redeploy and you can silently regress on security. Aiohttp in particular has critical CVEs: GHSA-8495-4g3g-x7pr (request smuggling, fixed in 3.10.11+) and GHSA-2vrm-gr82-f7m5 (fixed in 3.13.4+). Pin to known-safe floors and regenerate periodically.
🛡️ Suggested pinning
-aiohttp
-python-dotenv
-openai
-google-generativeai
-anthropic
+aiohttp>=3.13.4,<4
+python-dotenv>=1.0.1,<2
+openai>=1.40.0,<2
+google-generativeai>=0.8.3,<1
+anthropic>=0.34.0,<1🧰 Tools
🪛 OSV Scanner (2.3.6)
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP has CRLF injection through multipart part content type header construction
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP has late size enforcement for non-file multipart fields causes memory DoS
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP vulnerable to brute-force leak of internal static file path components
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP's C parser (llhttp) accepts null bytes and control characters in response header values - header injection/security bypass
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP's unicode processing of header values could cause parsing discrepancies
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP vulnerable to denial of service through large payloads
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP's HTTP Parser auto_decompress feature is vulnerable to zip bomb
[CRITICAL] 1-1: aiohttp 3.9.5: aiohttp allows request smuggling due to incorrect parsing of chunk extensions
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP is vulnerable to HTTP Request/Response Smuggling through incorrect parsing of chunked trailer sections
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP leaks Cookie and Proxy-Authorization headers on cross-origin redirect
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP accepts duplicate Host headers
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP Vulnerable to Cookie Parser Warning Storm
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP vulnerable to DoS through chunked messages
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP Affected by Denial of Service (DoS) via Unbounded DNS Cache in TCPConnector
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP vulnerable to DoS when bypassing asserts
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP has a Multipart Header Size Bypass
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP has unicode match groups in regexes for ASCII protocol elements
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP has HTTP response splitting via \r in reason phrase
[CRITICAL] 1-1: aiohttp 3.9.5: AIOHTTP affected by UNC SSRF/NTLMv2 Credential Theft/Local File Read in static resource handler on Windows
[CRITICAL] 1-1: aiohttp 3.9.5: aiohttp allows unlimited trailer headers, leading to possible uncapped memory usage
[HIGH] 1-1: tqdm 4.9.0: undefined
(PYSEC-2017-74)
[HIGH] 1-1: tqdm 4.9.0: tqdm CLI arguments injection attack
[HIGH] 1-1: tqdm 4.9.0: TDQM Arbitrary Code Execution
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@txmd5_predictor/requirements.txt` around lines 1 - 5, Update requirements.txt
to pin minimum-safe versions instead of leaving packages unpinned: replace bare
package lines for aiohttp, python-dotenv, openai, google-generativeai, and
anthropic with floor-pinned constraints (e.g., aiohttp>=3.13.4,
python-dotenv>=1.0.0, openai>=1.0.0, google-generativeai>=0.3.0,
anthropic>=0.4.0) to ensure reproducible, security-patched installs; after
updating, regenerate your lockfile or vendor list and add a note to periodically
review/upgrade these minimums.
1 participant
Added the
txmd5_predictorapplication to continuously analyze Sic Bo data and predict outcomes using a combination of custom algorithms and LLMs, sending automated hourly optimization reports to a Telegram bot.PR created automatically by Jules for task 3574055032524025026 started by @Tiengbip6110
Summary by CodeRabbit