app.py

import gradio as gr
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
from fastapi import FastAPI, Request
import uvicorn
from fastapi.middleware.cors import CORSMiddleware

# Create FastAPI app
app = FastAPI()

# Add CORS middleware to allow requests from your React Native app
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # In production, specify your actual domain
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load chatbot model
chatbot_model = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(chatbot_model)
model = AutoModelForCausalLM.from_pretrained(chatbot_model)

# Load emotion detection model
emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")

def generate_response(user_input):
    # Generate chatbot response
    input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
    output = model.generate(input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
    
    # Detect emotion
    emotion_result = emotion_pipeline(user_input)
    emotion = emotion_result[0]["label"]
    
    return response, emotion

# Create API endpoint
@app.post("/analyze")
async def analyze_text(request: Request):
    data = await request.json()
    user_input = data.get("text", "")
    
    if not user_input:
        return {"error": "No text provided"}
    
    response, emotion = generate_response(user_input)
    
    # Return structured response
    return {
        "response": response,
        "emotion": emotion,
        "score": emotion_pipeline(user_input)[0]["score"]
    }

# Create Gradio interface (optional, can keep for web testing)
iface = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(label="Enter your message"),
    outputs=[gr.Textbox(label="Chatbot Response"), gr.Textbox(label="Emotion Detected")],
    live=True
)

# Mount Gradio app to FastAPI
app = gr.mount_gradio_app(app, iface, path="/")

# Only needed if running directly
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)