main.py

import os
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
import shutil
import uvicorn
from fastapi import FastAPI, UploadFile, File, HTTPException
from pipeline import UltraRobustCallAnalytics
import gradio as gr

# --- 1. App & Pipeline Setup ---
app = FastAPI(title="Call Center Analytics Engine")
pipeline_engine = None

@app.on_event("startup")
async def startup_event():
    global pipeline_engine
    token = os.environ.get("HF_TOKEN")
    print(f"🔍 DEBUG: Checking for Token...")
    if token is None:
        print("❌ ERROR: HF_TOKEN is None! The app cannot read the secret.")
        print(f"   Available Environment Keys: {[k for k in os.environ.keys() if 'HF' in k]}")
    elif len(token) < 10:
        print("❌ ERROR: Token seems too short or invalid.")
    else:
        print(f"✅ Token found! Starts with: {token[:4]}...")

    # 3. Initialize
    print("Initializing UltraRobustCallAnalytics...")
    pipeline_engine = UltraRobustCallAnalytics(hf_token=token)
    print("Pipeline initialized successfully!")

# --- 2. Existing API Endpoint (for programmatic access) ---
@app.post("/analyze")
async def analyze_audio(file: UploadFile = File(...)):
    if not pipeline_engine:
        raise HTTPException(status_code=500, detail="Engine not initialized")

    temp_path = f"temp_{file.filename}"
    try:
        with open(temp_path, "wb") as buffer:
            shutil.copyfileobj(file.file, buffer)

        result = pipeline_engine.process_call(temp_path)
        return result

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
    finally:
        if os.path.exists(temp_path):
            os.remove(temp_path)

# --- 3. Gradio Wrapper Function ---
def gradio_process(audio_filepath):
    """
    Wrapper function to connect Gradio input directly to the pipeline.
    Gradio handles the file upload and provides a temp filepath.
    """
    if pipeline_engine is None:
        return {"error": "System is still starting up... please wait a moment."}
    
    if audio_filepath is None:
        return {"message": "Please upload a file."}
    
    try:
        # Call your existing pipeline logic directly
        print(f"Processing file from Gradio: {audio_filepath}")
        result = pipeline_engine.process_call(audio_filepath)
        return result
    except Exception as e:
        return {"error": str(e)}

# --- 4. Build Gradio UI ---
with gr.Blocks(title="Call Center AI") as demo:
    gr.Markdown("# 🎧 Call Center Analytics Hub")
    gr.Markdown("Upload a call recording to extract speakers, text, and emotions.")
    
    with gr.Row():
        with gr.Column():
            # Input: Audio file (returns a filepath)
            audio_input = gr.Audio(type="filepath", label="Upload or Record Call")
            analyze_btn = gr.Button("Analyze Call", variant="primary")
            
        with gr.Column():
            # Output: JSON result
            result_output = gr.JSON(label="Analysis Results")

    analyze_btn.click(fn=gradio_process, inputs=audio_input, outputs=result_output)

# --- 5. Mount Gradio & Run ---
# This serves the Gradio UI at the root "/"
app = gr.mount_gradio_app(app, demo, path="/")

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)