Update app.py

app.py

@@ -1,40 +1,65 @@
 import gradio as gr
-import requests
-
-# Define the sentiment analysis function that communicates with the Hugging Face model API
-def analyze_sentiment(message: str):
-    try:
-        # Send request to the Hugging Face Space for sentiment analysis
-        response = requests.post(
-            'https://safwansajad-emotion-detection-gpt.hf.space/predict',
-            json={'text': message},
-            headers={'Content-Type': 'application/json'}
-        )
-        
-        # Extract the sentiment and score from the response
-        data = response.json()
-        
-        # Return the label and score in the format expected by your app
-        if 'emotion' in data and 'score' in data:
-            return [{"label": data['emotion'], "score": data['score']}]
-        else:
-            return [{"label": "Unknown", "score": 0}]
-    
-    except Exception as e:
-        print(f"Error during sentiment analysis: {e}")
-        return [{"label": "Error", "score": 0}]
-
-# Set up Gradio interface
-iface = gr.Interface(
-    fn=analyze_sentiment,  # Function to be called
-    inputs=gr.Textbox(label="Enter your message", placeholder="How are you feeling today?", lines=2),  # User input
-    outputs=gr.JSON(),  # Output in JSON format (label and score)
-    live=True,  # Enables live input processing
-    title="Sentiment Analysis with SerenityAI",  # Title of the interface
-    description="Enter a message and get feedback about your emotional state. Your feelings matter!",
-    theme="huggingface",  # Optionally set the theme to Hugging Face's style
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+# 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"):
+    if session_id not in chat_histories:
+        chat_histories[session_id] = []
+
+    # 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
+
+# ------------------ Web Interface ------------------
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 Mental Health Chatbot")
+    with gr.Row():
+        with gr.Column():
+            chatbot = gr.Chatbot()
+            msg = gr.Textbox(label="Your Message")
+            session_id = gr.Textbox(label="Session ID", value="default")
+            btn = gr.Button("Send")
+            clear_btn = gr.Button("Clear History")
+        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])
+
+# ------------------ API Endpoint for /api/predict ------------------
+predict_api = gr.Interface(
+    fn=chatbot_response,
+    inputs=[gr.Textbox(label="Message"), gr.Textbox(label="Session ID")],
+    outputs=[gr.Textbox(label="Response"), gr.Textbox(label="Emotion"), gr.Number(label="Score")]
 )
 
-# Launch the Gradio app
-if __name__ == "__main__":
-    iface.launch(share=True)  # share=True gives you a public link
+# ------------------ Launch for Gradio Spaces ------------------
+demo.launch()
+predict_api.launch(inline=False)