corrected respond function

app.py

@@ -1,3 +1,4 @@
+'''
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import AutoPeftModelForCausalLM
@@ -80,5 +81,88 @@ demo = gr.ChatInterface(
     ],
 )
 
+if __name__ == "__main__":
+    demo.launch()
+'''
+
+import gradio as gr
+from transformers import AutoTokenizer
+from peft import AutoPeftModelForCausalLM
+import torch
+
+# Load the model and tokenizer
+def load_model():
+    lora_model_name = "sreyanghosh/lora_model"  # Replace with your LoRA model path
+    model = AutoPeftModelForCausalLM.from_pretrained(
+        lora_model_name,
+        load_in_4bit=False,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(lora_model_name)
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+    model.eval()
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = model.to(device)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Define the respond function
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    # Prepare the conversation history
+    messages = [{"role": "system", "content": system_message}]
+    for user_input, bot_response in history:
+        if user_input:
+            messages.append({"role": "user", "content": user_input})
+        if bot_response:
+            messages.append({"role": "assistant", "content": bot_response})
+    messages.append({"role": "user", "content": message})
+
+    # Format the input for the model
+    conversation_text = "\n".join(
+        f"{msg['role']}: {msg['content']}" for msg in messages
+    )
+    inputs = tokenizer(conversation_text, return_tensors="pt", truncation=True).to(model.device)
+    
+    # Generate the model's response
+    outputs = model.generate(
+        inputs.input_ids,
+        max_length=len(inputs.input_ids[0]) + max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Extract the new response
+    new_response = response.split("assistant:")[-1].strip()
+    yield new_response
+
+# Gradio app configuration
+demo = gr.ChatInterface(
+    fn=respond,
+    chatbot="Assistant",
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+)
+
 if __name__ == "__main__":
     demo.launch()