Update app.py

app.py

@@ -1,35 +1,31 @@
 import gradio as gr
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 
-# Load chatbot model and move it to CPU explicitly
+# Load chatbot model
 chatbot_model = "microsoft/DialoGPT-medium"
 tokenizer = AutoTokenizer.from_pretrained(chatbot_model)
-model = AutoModelForCausalLM.from_pretrained(chatbot_model).to('cpu')  # Ensure model is on CPU
+model = AutoModelForCausalLM.from_pretrained(chatbot_model)
 
-# Load emotion detection model and move it to CPU explicitly
-emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", device=-1)  # -1 means CPU
+# Load emotion detection model
+emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
 
-# Function to generate chatbot response and emotion analysis
 def generate_response(user_input):
     # Generate chatbot response
-    input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt").to('cpu')  # Ensure input tensor is on CPU
+    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
 
-    # Return chatbot response and detected emotion
-    return response, f"Emotion Detected: {emotion}"
-
-# Gradio interface setup
 iface = gr.Interface(
     fn=generate_response,
-    inputs=gr.Textbox(label="You:", placeholder="Enter your message here..."),
-    outputs=[gr.Textbox(label="Bot Response"), gr.Textbox(label="Emotion Detected")],
+    inputs=gr.Textbox(label="Enter your message"),
+    outputs=[gr.Textbox(label="Chatbot Response"), gr.Textbox(label="Emotion Detected")],
     live=True
 )
 
-# Launch the app
 iface.launch()