Download app.py from maxxcarl/mann: direct link, hf CLI and curl.
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- Download file 2.62 kB
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https://huggingface.co/spaces/maxxcarl/mann/resolve/main/app.py
- Command line
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hf download hf://spaces/maxxcarl/mann/app.py
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curl -L -o app.py https://huggingface.co/spaces/maxxcarl/mann/resolve/main/app.py
2.62 kB
| import os | |
| import logging | |
| from fastapi import FastAPI, HTTPException | |
| from pydantic import BaseModel | |
| from langchain_ollama import OllamaLLM | |
| from langchain_core.callbacks.manager import CallbackManager | |
| from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| from jira_mcp_connector import JiraMCPConnector | |
| from telegramchat import TelegramChatService | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| app = FastAPI(title="Qwen2.5 7B on HF Space") | |
| # ================== CHANGE HERE ================== | |
| MODEL_NAME = "qwen2.5:7b-instruct" # Best chat version | |
| # ================================================ | |
| def get_llm(): | |
| callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) | |
| return OllamaLLM( | |
| model=MODEL_NAME, | |
| temperature=0.7, | |
| num_ctx=8192, # Qwen supports bigger context | |
| callback_manager=callback_manager, | |
| ) | |
| class Question(BaseModel): | |
| text: str | |
| jira_connector = JiraMCPConnector( | |
| llm_factory=get_llm, | |
| logger=logger, | |
| ) | |
| telegram_chat_service = TelegramChatService( | |
| llm_factory=get_llm, | |
| token=os.getenv("TELEGRAM_TOKEN", ""), | |
| logger=logger, | |
| jira_connector=jira_connector, | |
| ) | |
| def read_root(): | |
| return { | |
| "message": "✅ Qwen2.5 7B is running!", | |
| "model": MODEL_NAME, | |
| "endpoints": ["/ask", "/stream"] | |
| } | |
| async def ask(question: Question): | |
| try: | |
| logger.info(f"Question: {question.text[:100]}...") | |
| llm = get_llm() | |
| response = llm.invoke(question.text) | |
| return { | |
| "question": question.text, | |
| "answer": response.strip() | |
| } | |
| except Exception as e: | |
| logger.error(f"Error: {e}") | |
| raise HTTPException(status_code=500, detail=str(e)) | |
| async def stream(): | |
| try: | |
| jira_connector.ensure_services_started() | |
| started = telegram_chat_service.start() | |
| return { | |
| "telegram_running": telegram_chat_service.is_running, | |
| "jira_api_running": True, | |
| "status": "started" if started else "already_running", | |
| "message": "Telegram + Jira MCP flow is active. Open Telegram and message your bot.", | |
| } | |
| except ValueError as e: | |
| raise HTTPException(status_code=400, detail=str(e)) | |
| except Exception as e: | |
| logger.error(f"Error starting Telegram chat: {e}") | |
| raise HTTPException(status_code=500, detail=str(e)) | |
| if __name__ == "__main__": | |
| import uvicorn | |
| uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PORT", 7860))) |