Update app.py

app.py

@@ -1,3 +1,43 @@
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-gr.load("models/codellama/CodeLlama-34b-Instruct-hf").launch()
+# Load the model and tokenizer
+model_name = "models/codellama/CodeLlama-34b-Instruct-hf"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+# Create a pipeline for chatbot interaction
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Function to handle chatbot interaction
+def chatbot_interaction(user_input, history=[]):
+    # Append user message to the conversation history
+    history.append(("user", user_input))
+
+    # Format the conversation history for the model
+    conversation = "\n".join([f"{role}: {message}" for role, message in history])
+
+    # Generate response from the model
+    response = pipe(conversation, max_length=1024, do_sample=True)[0]['generated_text']
+
+    # Extract the assistant's response and append it to the history
+    assistant_message = response[len(conversation):].strip()
+    history.append(("assistant", assistant_message))
+
+    return history, history
+
+# Gradio interface
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Your message:")
+    clear = gr.Button("Clear")
+
+    def respond(message, chat_history):
+        chat_history, updated_history = chatbot_interaction(message, chat_history)
+        return updated_history, ""
+
+    msg.submit(respond, [msg, chatbot], [chatbot, msg])
+    clear.click(lambda: None, None, chatbot)  # Clear the chat
+
+# Launch the Gradio app
+demo.launch()