Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Choose your model – here we use GPT-2 as an example
+model_name = "gpt2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_response(user_input, chat_history):
+    """
+    This function takes the user's input and current conversation history,
+    appends the input to the history, builds the conversation string, and 
+    generates a response using the local LLM.
+    """
+    if chat_history is None:
+        chat_history = []
+    
+    # Append the user message to the conversation history.
+    chat_history.append(("User", user_input))
+    
+    # Build a conversation string from the history.
+    conversation = ""
+    for speaker, message in chat_history:
+        conversation += f"{speaker}: {message}\n"
+    conversation += "AI:"  # Signal for the model to generate AI's response
+
+    # Tokenize the input and generate a response.
+    input_ids = tokenizer.encode(conversation, return_tensors="pt")
+    output_ids = model.generate(
+        input_ids, 
+        max_length=input_ids.shape[1] + 50,  # Adjust max_length as needed
+        pad_token_id=tokenizer.eos_token_id
+    )
+    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    # Extract only the AI response (everything after the last "AI:" prompt).
+    ai_response = generated_text[len(conversation):].strip().split("\n")[0]
+    chat_history.append(("AI", ai_response))
+    
+    # Return an empty string (to clear the input box) and updated chat history.
+    return "", chat_history
+
+# Build the Gradio interface using Blocks for a flexible layout.
+with gr.Blocks() as demo:
+    gr.Markdown("# Local LLM Chatbot")
+    
+    # Chatbot display widget
+    chatbot = gr.Chatbot()
+    
+    # Hidden state to hold the conversation history
+    state = gr.State([])
+    
+    # Textbox for user input
+    txt = gr.Textbox(placeholder="Enter your message and press Enter")
+    
+    # When the textbox is submitted, generate a response.
+    txt.submit(generate_response, [txt, state], [txt, chatbot])
+
+# Launch the interface
+demo.launch()