Update app.py

app.py

@@ -1,8 +1,10 @@
-from transformers import pipeline
+from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
 import gradio as gr
 
-# Load the chatbot pipeline
-chatbot = pipeline("conversational", model="facebook/blenderbot-400M-distill")
+# Load the model and tokenizer
+model_name = "facebook/blenderbot-400M-distill"
+tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
+model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
 
 # Initialize message history
 conversation_history = []
@@ -11,17 +13,21 @@ conversation_history = []
 def vanilla_chatbot(message, history):
     global conversation_history
     
-    # Create a conversation turn
-    conversation_history.append({"role": "user", "content": message})
+    # Append user message to history
+    conversation_history.append(message)
+    
+    # Encode the new user input, add the eos_token and return a tensor in Pytorch
+    inputs = tokenizer([message], return_tensors='pt')
     
     # Generate bot response
-    bot_response = chatbot(conversation_history)
+    reply_ids = model.generate(**inputs)
+    bot_response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
     
     # Append bot response to history
-    conversation_history.append({"role": "bot", "content": bot_response.generated_responses[-1]})
+    conversation_history.append(bot_response)
     
     # Return the generated response
-    return bot_response.generated_responses[-1]
+    return bot_response
 
 # Create a Gradio chat interface
 demo_chatbot = gr.Interface(
@@ -34,3 +40,4 @@ demo_chatbot = gr.Interface(
 
 # Launch the Gradio interface
 demo_chatbot.launch(share=True)
+