Update app.py

app.py

@@ -1,102 +1,45 @@
 import gradio as gr
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import json
 
 # Load models
 chatbot_model = "microsoft/DialoGPT-medium"
-tokenizer = AutoTokenizer.from_pretrained(chatbot_model)
-model = AutoModelForCausalLM.from_pretrained(chatbot_model)
-emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
-
-# Store chat histories
-chat_histories = {}
-
 def chatbot_response(message, session_id="default"):
-    """Core function that handles both chat and emotion analysis"""
     if session_id not in chat_histories:
         chat_histories[session_id] = []
-
-    # Generate chatbot response
+    
+    # Generate response
     input_ids = tokenizer.encode(message + 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(message)
     emotion = emotion_result[0]["label"]
     score = float(emotion_result[0]["score"])
-
+    
     # Store history
     chat_histories[session_id].append((message, response))
     return response, emotion, score
 
-# ------------------ API Interface ------------------
-def api_predict(message: str, session_id: str = "default"):
-    """Endpoint for /predict that returns JSON"""
-    response, emotion, score = chatbot_response(message, session_id)
-    return {
-        "response": response,
-        "emotion": emotion,
-        "score": score,
-        "session_id": session_id
-    }
-
-# ------------------ Web Interface ------------------
-with gr.Blocks(title="Mental Health Chatbot") as web_interface:
+# Gradio Interface (Primary for Spaces)
+with gr.Blocks() as demo:
     gr.Markdown("# 🤖 Mental Health Chatbot")
-    
     with gr.Row():
-        with gr.Column():
-            chatbot = gr.Chatbot(height=400)
-            msg = gr.Textbox(placeholder="Type your message...", label="You")
-            with gr.Row():
-                session_id = gr.Textbox(label="Session ID", value="default")
-                submit_btn = gr.Button("Send", variant="primary")
-                clear_btn = gr.Button("Clear")
-        
-        with gr.Column():
-            emotion_out = gr.Textbox(label="Detected Emotion")
-            score_out = gr.Number(label="Confidence Score")
 
-    def respond(message, chat_history, session_id):
-        response, emotion, score = chatbot_response(message, session_id)
-        chat_history.append((message, response))
-        return "", chat_history, emotion, score
+    btn.click(respond, [msg, chatbot, session_id], [msg, chatbot, emotion_out, score_out])
+    msg.submit(respond, [msg, chatbot, session_id], [msg, chatbot, emotion_out, score_out])
+    clear_btn.click(lambda s_id: ([], "", 0.0) if s_id in chat_histories else ([], "", 0.0), 
+                   inputs=[session_id], 
+                   outputs=[chatbot, emotion_out, score_out])
+
+
+
+
 
-    submit_btn.click(
-        respond,
-        [msg, chatbot, session_id],
-        [msg, chatbot, emotion_out, score_out]
-    )
-    msg.submit(
-        respond,
-        [msg, chatbot, session_id],
-        [msg, chatbot, emotion_out, score_out]
-    )
-    clear_btn.click(
-        lambda s_id: ([], "", 0.0) if s_id in chat_histories else ([], "", 0.0),
-        [session_id],
-        [chatbot, emotion_out, score_out]
-    )
 
-# ------------------ Mount Interfaces ------------------
-app = gr.mount_gradio_app(
-    gr.routes.App(),
-    web_interface,
-    path="/"
-)
 
-app = gr.mount_gradio_app(
-    app,
-    gr.Interface(
-        fn=api_predict,
-        inputs=[gr.Textbox(), gr.Textbox()],
-        outputs=gr.JSON(),
-        title="API Predict",
-        description="Use this endpoint for programmatic access"
-    ),
-    path="/predict"
-)
 
-# ------------------ Launch ------------------
+# For Hugging Face Spaces, Gradio must be the main interface
 if __name__ == "__main__":
-    app.launch(show_api=False)  # We manually mounted our API
+    demo.launch()