Update app.py

app.py

@@ -1,18 +1,18 @@
 import gradio as gr
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 
-# Load chatbot model
+# Load chatbot model and move it to CPU explicitly
 chatbot_model = "microsoft/DialoGPT-medium"
 tokenizer = AutoTokenizer.from_pretrained(chatbot_model)
-model = AutoModelForCausalLM.from_pretrained(chatbot_model)
+model = AutoModelForCausalLM.from_pretrained(chatbot_model).to('cpu')  # Ensure model is on CPU
 
-# Load emotion detection model
-emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
+# 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
 
 # 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")
+    input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt").to('cpu')  # Ensure input tensor is on CPU
     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)
 
@@ -25,4 +25,11 @@ def generate_response(user_input):
 
 # Gradio interface setup
 iface = gr.Interface(
-    fn
+    fn=generate_response,
+    inputs=gr.Textbox(label="You:", placeholder="Enter your message here..."),
+    outputs=[gr.Textbox(label="Bot Response"), gr.Textbox(label="Emotion Detected")],
+    live=True
+)
+
+# Launch the app
+iface.launch()