app.py

import gradio as gr
import spaces
import torch

import transformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
model_name = "mistralai/Mistral-7B-Instruct-v0.2"
pipeline = transformers.pipeline(
    "text-generation",
    model=model_name,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cpu",
)

def chat_function(message, history, system_prompt, max_new_tokens, temperature):
    messages = []
    # Check if history is None or empty and handle accordingly
    if history:
        for user_msg, assistant_msg in history:
            messages.append({"role": "user", "content": user_msg})
            messages.append({"role": "assistant", "content": assistant_msg})
    
    # Always add the current user message
    messages.append({"role": "user", "content": message})
    
    # Construct the prompt using the pipeline's tokenizer
    prompt = pipeline.tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

    # Generate the response
    terminators = [
        pipeline.tokenizer.eos_token_id,
        pipeline.tokenizer.convert_tokens_to_ids("")
    ]

    # Adjust the temperature slightly above given to ensure variety
    adjusted_temp = temperature + 0.1

    # Generate outputs with adjusted parameters
    outputs = pipeline(
        prompt,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        temperature=adjusted_temp,
        top_p=0.9
    )

    # Extract the generated text, skipping the length of the prompt
    generated_text = outputs[0]["generated_text"]
    return generated_text[len(prompt):]  # Return the new part of the conversation

# Update Gradio interface setup
gr.Interface(
    fn=chat_function,
    inputs=[
        gr.Textbox(placeholder="Enter your message here", label="Your Message"),
        gr.JSON(label="Conversation History (format as [[user, assistant], ...])"),  # Without optional
        gr.Textbox(label="System Prompt"),
        gr.Slider(512, 4096, label="Max New Tokens"),
        gr.Slider(0.0, 1.0, step=0.1, label="Temperature")
    ],
    outputs=gr.Textbox(label="AI Response")
).launch()




# def chat_function(message, history, system_prompt,max_new_tokens,temperature):
#     messages = [
#         {"role": "system", "content": system_prompt},
#         {"role": "user", "content": message},
#     ]
#     prompt = pipeline.tokenizer.apply_chat_template(
#         messages,
#         tokenize=False,
#         add_generation_prompt=True
#     )
#     terminators = [
#         pipeline.tokenizer.eos_token_id,
#         pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
#     ]
#     temp = temperature + 0.1
#     outputs = pipeline(
#         prompt,
#         max_new_tokens=max_new_tokens,
#         eos_token_id=terminators,
#         do_sample=True,
#         temperature=temp,
#         top_p=0.9,
#     )
#     return outputs[0]["generated_text"][len(prompt):]

# gr.ChatInterface(
#     chat_function,
#     chatbot=gr.Chatbot(height=400),
#     textbox=gr.Textbox(placeholder="Enter message here", container=False, scale=7),
#     title="Meta-Llama-3-8B-Instruct",
#     description="""
#     To Learn about Fine-tuning Llama-3-8B, Ckeck https://exnrt.com/blog/ai/finetune-llama3-8b/.
#     """,
#     additional_inputs=[
#         gr.Textbox("You are helpful AI.", label="System Prompt"),
#         gr.Slider(512, 4096, label="Max New Tokens"),
#         gr.Slider(0, 1, label="Temperature")
#     ]
# ).launch()


#The Code

# import gradio as gr
# import os
# import spaces
# from transformers import GemmaTokenizer, AutoModelForCausalLM
# from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
# from threading import Thread

# # Set an environment variable
# HF_TOKEN = os.environ.get("HF_TOKEN", None)


# DESCRIPTION = '''
# <div>
# <h1 style="text-align: center;">Meta Llama3 8B</h1>
# <p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct"><b>Meta Llama3 8b Chat</b></a>. Meta Llama3 is the new open LLM and comes in two sizes: 8b and 70b. Feel free to play with it, or duplicate to run privately!</p>
# <p>🔎 For more details about the Llama3 release and how to use the model with <code>transformers</code>, take a look <a href="https://huggingface.co/blog/llama3">at our blog post</a>.</p>
# <p>🦕 Looking for an even more powerful model? Check out the <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> integration for Meta Llama 3 70b</p>
# </div>
# '''

# LICENSE = """
# <p/>
# ---
# Built with Meta Llama 3
# """

# PLACEHOLDER = """
# <div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
#    <img src="https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/8e75e61cc9bab22b7ce3dec85ab0e6db1da5d107/Meta_lockup_positive%20primary_RGB.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55;  "> 
#    <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Meta llama3</h1>
#    <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
# </div>
# """


# css = """
# h1 {
#   text-align: center;
#   display: block;
# }
# #duplicate-button {
#   margin: auto;
#   color: white;
#   background: #1565c0;
#   border-radius: 100vh;
# }
# """

# # Load the tokenizer and model
# tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
# model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto")  # to("cuda:0") 
# terminators = [
#     tokenizer.eos_token_id,
#     tokenizer.convert_tokens_to_ids("<|eot_id|>")
# ]

# @spaces.GPU(duration=120)
# def chat_llama3_8b(message: str, 
#               history: list, 
#               temperature: float, 
#               max_new_tokens: int
#              ) -> str:
#     """
#     Generate a streaming response using the llama3-8b model.
#     Args:
#         message (str): The input message.
#         history (list): The conversation history used by ChatInterface.
#         temperature (float): The temperature for generating the response.
#         max_new_tokens (int): The maximum number of new tokens to generate.
#     Returns:
#         str: The generated response.
#     """
#     conversation = []
#     for user, assistant in history:
#         conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
#     conversation.append({"role": "user", "content": message})

#     input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
    
#     streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)

#     generate_kwargs = dict(
#         input_ids= input_ids,
#         streamer=streamer,
#         max_new_tokens=max_new_tokens,
#         do_sample=True,
#         temperature=temperature,
#         eos_token_id=terminators,
#     )
#     # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.             
#     if temperature == 0:
#         generate_kwargs['do_sample'] = False
        
#     t = Thread(target=model.generate, kwargs=generate_kwargs)
#     t.start()

#     outputs = []
#     for text in streamer:
#         outputs.append(text)
#         print(outputs)
#         yield "".join(outputs)
        

# # Gradio block
# chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')

# with gr.Blocks(fill_height=True, css=css) as demo:
    
#     gr.Markdown(DESCRIPTION)
#     gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
#     gr.ChatInterface(
#         fn=chat_llama3_8b,
#         chatbot=chatbot,
#         fill_height=True,
#         additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
#         additional_inputs=[
#             gr.Slider(minimum=0,
#                       maximum=1, 
#                       step=0.1,
#                       value=0.95, 
#                       label="Temperature", 
#                       render=False),
#             gr.Slider(minimum=128, 
#                       maximum=4096,
#                       step=1,
#                       value=512, 
#                       label="Max new tokens", 
#                       render=False ),
#             ],
#         examples=[
#             ['How to setup a human base on Mars? Give short answer.'],
#             ['Explain theory of relativity to me like I’m 8 years old.'],
#             ['What is 9,000 * 9,000?'],
#             ['Write a pun-filled happy birthday message to my friend Alex.']
#             ],
#         cache_examples=False,
#                      )
    
#     gr.Markdown(LICENSE)
    
# if __name__ == "__main__":
#     demo.launch()