app.py

# # import gradio as gr
# # from huggingface_hub import InferenceClient

# # """
# # For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
# # """
# # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


# # def respond(
# #     message,
# #     history: list[tuple[str, str]],
# #     system_message,
# #     max_tokens,
# #     temperature,
# #     top_p,
# # ):
# #     messages = [{"role": "system", "content": system_message}]

# #     for val in history:
# #         if val[0]:
# #             messages.append({"role": "user", "content": val[0]})
# #         if val[1]:
# #             messages.append({"role": "assistant", "content": val[1]})

# #     messages.append({"role": "user", "content": message})

# #     response = ""

# #     for message in client.chat_completion(
# #         messages,
# #         max_tokens=max_tokens,
# #         stream=True,
# #         temperature=temperature,
# #         top_p=top_p,
# #     ):
# #         token = message.choices[0].delta.content

# #         response += token
# #         yield response


# # """
# # For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
# # """
# # demo = gr.ChatInterface(
# #     respond,
# #     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()

import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
from safetensors.torch import load_file, save_file

# Define model names
# MODEL_1_PATH = "./adapter_model.safetensors"  # Local path inside Space
###
MODEL_1_PATH = "Priyanka6/fine-tuning-inference"
###
MODEL_2_NAME = "sarvamai/sarvam-1"  # The base model on Hugging Face Hub
# MODEL_3_NAME = 

def trim_adapter_weights(model_path):
    """
    Trims the last token from the adapter's lm_head.lora_B.default.weight 
    if there is a mismatch with the base model.
    """
    model_path = "./adapter_model.safetensors"
    # if not os.path.exists(model_path):
    #     raise FileNotFoundError(f"Adapter file not found: {model_path}")
    
    checkpoint = load_file(model_path)
    print("Keys in checkpoint:", list(checkpoint.keys()))

    key_to_trim = "lm_head.lora_B.default.weight"
    
    if key_to_trim in checkpoint:
        print("Entered")
        original_size = checkpoint[key_to_trim].shape[0]
        expected_size = original_size - 1  # Removing last token
        
        print(f"Trimming {key_to_trim}: {original_size} -> {expected_size}")

        checkpoint[key_to_trim] = checkpoint[key_to_trim][:-1]  # Trim the last row

        # Save the modified adapter
        trimmed_adapter_path = os.path.join(model_path, "adapter_model_trimmed.safetensors")
        save_file(checkpoint, trimmed_adapter_path)
        return trimmed_adapter_path
    print("did execute the if block")
    return model_path
model_path=os.path.join(MODEL_1_PATH,"adapter_model.safetensors")
trimmed_adapter_path = trim_adapter_weights(model_path)

# Load the tokenizer (same for both models)
TOKENIZER_NAME = "sarvamai/sarvam-1"
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)

# Function to load a model
def load_model(model_choice):
    if model_choice == "Hugging face dataset":
        model = AutoModelForCausalLM.from_pretrained("./", torch_dtype=torch.float16, device_map="auto")
        trimmed_adapter_path = os.path.join("Priyanka6/fine-tuning-inference", "adapter_model_trimmed.safetensors")
        model.load_adapter(trimmed_adapter_path, "safe_tensors")  # Load safetensors adapter
    else:
        model = AutoModelForCausalLM.from_pretrained(MODEL_2_NAME)
    model.eval()
    return model

# Load default model on startup
current_model = load_model("Hugging face dataset")

# Chatbot response function
def respond(message, history, model_choice, max_tokens, temperature, top_p):
    global current_model
    
    # Switch model if user selects a different one
    if (model_choice == "Hugging face dataset" and current_model is not None and current_model.config.name_or_path != MODEL_1_PATH) or \
       (model_choice == "Proprietary dataset1" and current_model is not None and current_model.config.name_or_path != MODEL_2_NAME):
        current_model = load_model(model_choice)

    # Convert chat history to format
    messages = [{"role": "system", "content": "You are a friendly AI assistant."}]
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})
    messages.append({"role": "user", "content": message})

    # Tokenize and generate response
    inputs = tokenizer.apply_chat_template(messages, tokenize=False)
    input_tokens = tokenizer(inputs, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")

    output_tokens = current_model.generate(
        **input_tokens,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

    response = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
    return response

# Define Gradio Chat Interface
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Dropdown(choices=["Hugging face dataset", "Proprietary dataset1"], value="Fine-Tuned Model", label="Select Model"),
        gr.Slider(minimum=1, maximum=1024, value=256, step=1, label="Max Tokens"),
        gr.Slider(minimum=0.1, maximum=2.0, 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"),
    ],
)

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


# # Test the chatbot
# if __name__ == "__main__":
#     while True:
#         query = input("User: ")
#         if query.lower() in ["exit", "quit"]:
#             break
#         response = chat(query)
#         print(f"Bot: {response}")