Update app.py

app.py

@@ -1,5 +1,20 @@
 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"
@@ -14,13 +29,32 @@ def generate_response(user_input):
     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"),
@@ -28,4 +62,9 @@ iface = gr.Interface(
     live=True
 )
 
-iface.launch()
+# 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)