Delete app.py

app.py

@@ -1,170 +0,0 @@
-"""
-CryptoSentinel AI — High-performance FastAPI application.
-
-This is the main entry point that orchestrates the entire application.
-- Integrates the asynchronous PriceFetcher for live market data.
-- Integrates the asynchronous SentimentAnalyzer for real-time analysis.
-- Serves the interactive frontend and provides all necessary API endpoints.
-"""
-import asyncio
-import json
-from contextlib import asynccontextmanager
-
-import httpx
-from fastapi import FastAPI, Request, BackgroundTasks
-from fastapi.responses import HTMLResponse, StreamingResponse
-from fastapi.templating import Jinja2Templates
-from pydantic import BaseModel, constr
-
-# ====================================================================
-#                       FIX APPLIED HERE
-# ====================================================================
-# Use relative imports because price_fetcher and sentiment are in the same 'app' directory.
-from .price_fetcher import PriceFetcher
-from .sentiment import SentimentAnalyzer
-# ====================================================================
-
-
-# --- Pydantic Model for API Input Validation ---
-
-class SentimentRequest(BaseModel):
-    """Ensures the text for sentiment analysis is a non-empty string."""
-    text: constr(strip_whitespace=True, min_length=1)
-
-# --- Application Lifespan for Resource Management ---
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    """
-    Manages application startup and shutdown events using the modern
-    lifespan context manager.
-    """
-    # On startup:
-    async with httpx.AsyncClient() as client:
-        # Instantiate and store our services in the application state.
-        # This makes them accessible in any request handler via `request.app.state`.
-        app.state.price_fetcher = PriceFetcher(client=client, coins=["bitcoin", "ethereum", "dogecoin"])
-        app.state.sentiment_analyzer = SentimentAnalyzer(client=client)
-        app.state.request_counter = 0  # Simple counter for unique SSE event IDs
-
-        # Create a cancellable background task for continuous price updates.
-        price_update_task = asyncio.create_task(
-            run_periodic_updates(app.state.price_fetcher, interval_seconds=10)
-        )
-        
-        print("🚀 CryptoSentinel AI started successfully.")
-        yield  # The application is now running and ready to accept requests.
-        
-        # On shutdown:
-        print("⏳ Shutting down background tasks...")
-        price_update_task.cancel()
-        try:
-            await price_update_task
-        except asyncio.CancelledError:
-            print("Price update task cancelled successfully.")
-        print("✅ Shutdown complete.")
-
-async def run_periodic_updates(fetcher: PriceFetcher, interval_seconds: int):
-    """A robust asyncio background task that periodically updates prices."""
-    while True:
-        await fetcher.update_prices_async()
-        await asyncio.sleep(interval_seconds)
-
-# --- FastAPI App Initialization ---
-
-app = FastAPI(title="CryptoSentinel AI", lifespan=lifespan)
-# Assuming your project structure is /app/templates
-templates = Jinja2Templates(directory="app/templates")
-
-# --- API Endpoints ---
-
-@app.get("/", response_class=HTMLResponse)
-async def serve_dashboard(request: Request):
-    """Serves the main interactive dashboard from `index.html`."""
-    return templates.TemplateResponse("index.html", {"request": request})
-
-@app.get("/api/prices", response_class=HTMLResponse)
-async def get_prices_fragment(request: Request):
-    """Returns an HTML fragment with the latest cached crypto prices for HTMX."""
-    price_fetcher: PriceFetcher = request.app.state.price_fetcher
-    prices = price_fetcher.get_current_prices()
-
-    html_fragment = ""
-    for coin, price in prices.items():
-        # Format the price nicely, handling the initial '--' state
-        price_str = f"${price:,.2f}" if isinstance(price, (int, float)) else price
-        html_fragment += f"<div><strong>{coin.capitalize()}:</strong> {price_str}</div>"
-    
-    return HTMLResponse(content=html_fragment)
-
-@app.post("/api/sentiment")
-async def analyze_sentiment(
-    payload: SentimentRequest,
-    request: Request,
-    background_tasks: BackgroundTasks
-):
-    """
-    Validates and queues a text for sentiment analysis. The heavy lifting is
-    done in the background to ensure the API responds instantly.
-    """
-    analyzer: SentimentAnalyzer = request.app.state.sentiment_analyzer
-    request.app.state.request_counter += 1
-    request_id = request.app.state.request_counter
-
-    # The actual API call to Hugging Face will run after this response is sent.
-    background_tasks.add_task(analyzer.compute_and_publish, payload.text, request_id)
-    
-    return HTMLResponse(content="<small>Queued for analysis...</small>")
-
-@app.get("/api/sentiment/stream")
-async def sentiment_stream(request: Request):
-    """
-    Establishes a Server-Sent Events (SSE) connection. It efficiently pushes
-    new sentiment results as HTML fragments to the client as they become available.
-    """
-    analyzer: SentimentAnalyzer = request.app.state.sentiment_analyzer
-
-    async def event_generator():
-        # Clear the initial "waiting..." message on the client.
-        # hx-swap-oob="innerHTML" swaps this div out-of-band without affecting the target.
-        yield f"event: sentiment_update\ndata: <div id='sentiment-results' hx-swap-oob='innerHTML'></div>\n\n"
-        
-        # Listen for new results from the analyzer's internal queue.
-        async for result_payload in analyzer.stream_results():
-            try:
-                result = result_payload['result']
-                label = str(result.get('label', 'NEUTRAL')).lower()
-                score = result.get('score', 0.0) * 100
-                text = result_payload['text']
-                
-                # Dynamically build the HTML fragment to be sent to the client.
-                html_fragment = f"""
-                <div>
-                    <blockquote>{text}</blockquote>
-                    <p>
-                        <strong>Result:</strong> 
-                        <span class="sentiment-{label}">{label.upper()}</span> 
-                        (Confidence: {score:.1f}%)
-                    </p>
-                </div>
-                """
-                # First, process the string to remove newlines. This avoids a
-                # backslash in the f-string expression, fixing the SyntaxError.
-                data_payload = html_fragment.replace('\n', '')
-                
-                # Then, use the clean variable in the f-string to build the message.
-                sse_message = f"event: sentiment_update\ndata: {data_payload}\n\n"
-                
-                yield sse_message
-
-            except (KeyError, TypeError):
-                continue # Ignore malformed payloads
-
-    return StreamingResponse(event_generator(), media_type="text/event-stream")
-
-# --- Main execution block for local development ---
-if __name__ == "__main__":
-    import uvicorn
-    # This run command assumes you are running `python app.py` from within the /app directory
-    # or `python -m app.app` from the parent directory.
-    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)