# Define request body model
class InputText(BaseModel):
    text: str

# Load pre-trained sentiment analysis pipeline
pipe = pipeline("text-classification", model="avichr/heBERT_sentiment_analysis")

# Define sentiment analysis endpoint
@app.post("/analyze_sentiment/")
async def analyze_sentiment(input_text: InputText):
    try:
        # Perform sentiment analysis
        result = pipe(input_text.text)
        
        # Format response
        response_data = {
            "text": input_text.text,
            "sentiment": result[0]["label"],
            "confidence": result[0]["score"]
        }
        
        # Return response
        return JSONResponse(content=response_data)
    except Exception as e:
        # Return error response if an exception occurs
        return JSONResponse(status_code=500, content={"error": str(e)})

# Homepage endpoint
@app.get("/")
async def home():
    return RedirectResponse(url="/docs")

# Run the FastAPI app
if __name__ == "__main__":
    import nest_asyncio
    nest_asyncio.apply()
    ngrok_tunnel = ngrok.connect(8000)
    print("Public URL:", ngrok_tunnel.public_url)
    try:
        import uvicorn
        uvicorn.run(app, host="0.0.0.0", port=8000)
    except Exception as e:
        print(e)