Create app.py

app.py

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+from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
+import gradio as gr
+
+# 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 = []
+
+# Function to interact with the chatbot
+def vanilla_chatbot(message, history):
+    global conversation_history
+    
+    # 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
+    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(bot_response)
+    
+    # Return the generated response
+    return bot_response
+
+    # Create a Gradio chat interface
+demo_chatbot = gr.Interface(
+    fn=vanilla_chatbot,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your message here..."),
+    outputs=gr.Textbox(placeholder="Bot response will appear here..."),
+    title="Mashdemy Chatbot",
+    description="Enter text to start chatting."
+)
+
+# Launch the Gradio interface
+demo_chatbot.launch(share=True)