app.py

# import torch
# from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaConfig
# import gradio as gr


# # Model IDs from Hugging Face Hub
# base_model_id = "HuggingFaceTB/SmolLM2-135M"
# instruct_model_id = "MaxBlumenfeld/smollm2-135m-bootleg-instruct-01"

# # Load tokenizer
# base_tokenizer = AutoTokenizer.from_pretrained(base_model_id)

# # Load models with explicit LLaMA architecture
# base_model = LlamaForCausalLM.from_pretrained(base_model_id)
# instruct_model = LlamaForCausalLM.from_pretrained(instruct_model_id)

# def generate_response(model, tokenizer, message, temperature=0.5, max_length=200, system_prompt="", is_instruct=False):
#     # Prepare input based on model type
#     if is_instruct:
#         if system_prompt:
#             full_prompt = f"{system_prompt}\n\nHuman: {message}\nAssistant:"
#         else:
#             full_prompt = f"Human: {message}\nAssistant:"
#     else:
#         # For base model, use simpler prompt format
#         full_prompt = message
    
#     inputs = tokenizer(full_prompt, return_tensors="pt")
    
#     with torch.no_grad():
#         outputs = model.generate(
#             inputs.input_ids,
#             max_length=max_length,
#             do_sample=True,
#             temperature=temperature,
#             top_k=50,
#             top_p=0.95,
#             num_return_sequences=1,
#             pad_token_id=tokenizer.eos_token_id  # Add padding token
#         )
        
#     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
#     if is_instruct:
#         try:
#             response = response.split("Assistant:")[-1].strip()
#         except:
#             pass
#     else:
#         response = response[len(full_prompt):].strip()
        
#     return response

# def chat(message, temperature, max_length, system_prompt):
#     # Generate responses from both models
#     base_response = generate_response(
#         base_model, 
#         base_tokenizer, 
#         message, 
#         temperature, 
#         max_length, 
#         system_prompt,
#         is_instruct=False
#     )
    
#     instruct_response = generate_response(
#         instruct_model, 
#         base_tokenizer, 
#         message, 
#         temperature, 
#         max_length, 
#         system_prompt,
#         is_instruct=True
#     )
    
#     return base_response, instruct_response

# # Create Gradio interface
# with gr.Blocks() as demo:
#     gr.Markdown("# SmolLM2-135M Comparison Demo")
#     gr.Markdown("Compare responses between base and fine-tuned versions of SmolLM2-135M")
    
#     with gr.Row():
#         with gr.Column():
#             message_input = gr.Textbox(label="Input Message")
#             system_prompt = gr.Textbox(
#                 label="System Prompt (Optional)",
#                 placeholder="Set context or personality for the model",
#                 lines=3
#             )
            
#         with gr.Column():
#             temperature = gr.Slider(
#                 minimum=0.1, 
#                 maximum=2.0, 
#                 value=0.5, 
#                 label="Temperature"
#             )
#             max_length = gr.Slider(
#                 minimum=50, 
#                 maximum=500, 
#                 value=200, 
#                 step=10, 
#                 label="Max Length"
#             )
    
#     with gr.Row():
#         with gr.Column():
#             gr.Markdown("### Base Model Response")
#             base_output = gr.Textbox(label="Base Model (SmolLM2-135M)", lines=5)
            
#         with gr.Column():
#             gr.Markdown("### Bootleg Instruct Model Response") 
#             instruct_output = gr.Textbox(label="Fine-tuned Model", lines=5)
    
#     submit_btn = gr.Button("Generate Responses")
#     submit_btn.click(
#         fn=chat,
#         inputs=[message_input, temperature, max_length, system_prompt],
#         outputs=[base_output, instruct_output]
#     )

# if __name__ == "__main__":
#     demo.launch()



from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import gradio as gr

# model_id = "HuggingFaceTB/SmolLM2-135M"
model_id = "MaxBlumenfeld/smollm2-135m-bootleg-instruct04"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

def generate_response(message, temperature=0.7, max_length=200):
   prompt = f"Human: {message}\nAssistant:"
   inputs = tokenizer(prompt, return_tensors="pt")
   
   with torch.no_grad():
       outputs = model.generate(
           inputs.input_ids,
           max_length=max_length,
           temperature=temperature,
           do_sample=True,
           pad_token_id=tokenizer.eos_token_id
       )
   
   response = tokenizer.decode(outputs[0], skip_special_tokens=True)
   return response.split("Assistant:")[-1].strip()

with gr.Blocks() as demo:
   gr.Markdown("# SmolLM2 Bootleg Instruct Chat")
   
   with gr.Row():
       with gr.Column():
           message = gr.Textbox(label="Message")
           temp = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, label="Temperature")
           max_len = gr.Slider(minimum=50, maximum=500, value=200, label="Max Length") 
           submit = gr.Button("Send")
           
       with gr.Column():
           output = gr.Textbox(label="Response")
           
   submit.click(
       generate_response,
       inputs=[message, temp, max_len],
       outputs=output
   )

if __name__ == "__main__":
   demo.launch()