Update app.py

app.py

@@ -1,20 +1,5 @@
 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"
@@ -33,38 +18,20 @@ def generate_response(user_input):
     # Detect emotion
     emotion_result = emotion_pipeline(user_input)
     emotion = emotion_result[0]["label"]
+    score = emotion_result[0]["score"]
     
-    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"]
-    }
+    return response, emotion, score
 
-# Create Gradio interface (optional, can keep for web testing)
+# Gradio Interface
 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
+    outputs=[
+        gr.Textbox(label="Chatbot Response"),
+        gr.Textbox(label="Emotion Detected"),
+        gr.Textbox(label="Emotion Score")
+    ],
+    live=False
 )
 
-# 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)
+iface.launch()