app.py

import json
import os
import shutil
import requests

import gradio as gr
from huggingface_hub import Repository, InferenceClient

HF_TOKEN = os.environ.get("HF_TOKEN", None)
API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-180B-chat"
BOT_NAME = "Falcon"

STOP_SEQUENCES = ["\nUser:", "<|endoftext|>", " User:", "###"]

EXAMPLES = [
    ["Hey Falcon! Any recommendations for my holidays in Abu Dhabi?"],
    ["What's the Everett interpretation of quantum mechanics?"],
    ["Give me a list of the top 10 dive sites you would recommend around the world."],
    ["Can you tell me more about deep-water soloing?"],
    ["Can you write a short tweet about the release of our latest AI model, Falcon LLM?"]
    ]

client = InferenceClient(
    API_URL,
    headers={"Authorization": f"Bearer {HF_TOKEN}"},
)

# def format_prompt(message, history, system_prompt):
#   prompt = ""
#   if system_prompt:
#     prompt += f"System: {system_prompt}\n"
#   for user_prompt, bot_response in history:
#     prompt += f"User: {user_prompt}\n"
#     prompt += f"Falcon: {bot_response}\n" # Response already contains "Falcon: "
#   prompt += f"""User: {message}
# Falcon:"""
#   return prompt

# seed = 42

# def generate(
#     prompt, history, system_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
# ):
#     temperature = float(temperature)
#     if temperature < 1e-2:
#         temperature = 1e-2
#     top_p = float(top_p)
#     global seed
#     generate_kwargs = dict(
#         temperature=temperature,
#         max_new_tokens=max_new_tokens,
#         top_p=top_p,
#         repetition_penalty=repetition_penalty,
#         stop_sequences=STOP_SEQUENCES,
#         do_sample=True,
#         seed=seed,
#     )
#     seed = seed + 1
#     formatted_prompt = format_prompt(prompt, history, system_prompt)

#     stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
#     output = ""

#     for response in stream:
#         output += response.token.text

#         for stop_str in STOP_SEQUENCES:
#             if output.endswith(stop_str):
#                 output = output[:-len(stop_str)]
#                 output = output.rstrip()
#                 yield output
#         yield output
#     return output


# additional_inputs=[
#     gr.Textbox("", label="Optional system prompt"),
#     gr.Slider(
#         label="Temperature",
#         value=0.9,
#         minimum=0.0,
#         maximum=1.0,
#         step=0.05,
#         interactive=True,
#         info="Higher values produce more diverse outputs",
#     ),
#     gr.Slider(
#         label="Max new tokens",
#         value=256,
#         minimum=0,
#         maximum=8192,
#         step=64,
#         interactive=True,
#         info="The maximum numbers of new tokens",
#     ),
#     gr.Slider(
#         label="Top-p (nucleus sampling)",
#         value=0.90,
#         minimum=0.0,
#         maximum=1,
#         step=0.05,
#         interactive=True,
#         info="Higher values sample more low-probability tokens",
#     ),
#     gr.Slider(
#         label="Repetition penalty",
#         value=1.2,
#         minimum=1.0,
#         maximum=2.0,
#         step=0.05,
#         interactive=True,
#         info="Penalize repeated tokens",
#     )
# ]


# with gr.Blocks() as demo:
#     with gr.Row():
#         with gr.Column(scale=0.4):
#             gr.Image("better_banner.jpeg", elem_id="banner-image", show_label=False)
#         with gr.Column():
#             gr.Markdown(
#                 """# Falcon-180B Demo

#                 **Chat with [Falcon-180B-Chat](https://huggingface.co/tiiuae/falcon-180b-chat), brainstorm ideas, discuss your holiday plans, and more!**
                
#                 ✨ This demo is powered by [Falcon-180B](https://huggingface.co/tiiuae/falcon-180B) and finetuned on a mixture of [Ultrachat](https://huggingface.co/datasets/stingning/ultrachat), [Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) and [Airoboros](https://huggingface.co/datasets/jondurbin/airoboros-2.1). [Falcon-180B](https://huggingface.co/tiiuae/falcon-180b) is a state-of-the-art large language model built by the [Technology Innovation Institute](https://www.tii.ae) in Abu Dhabi. It is trained on 3.5 trillion tokens (including [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)) and available under the [Falcon-180B TII License](https://huggingface.co/spaces/tiiuae/falcon-180b-license/blob/main/LICENSE.txt). It currently holds the 🥇 1st place on the [🤗 Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for a pretrained model. 
                
#                 🧪 This is only a **first experimental preview**: we intend to provide increasingly capable versions of Falcon in the future, based on improved datasets and RLHF/RLAIF.
                
#                 👀 **Learn more about Falcon LLM:** [falconllm.tii.ae](https://falconllm.tii.ae/)
                
#                 ➡️️ **Intended Use**: this demo is intended to showcase an early finetuning of [Falcon-180B](https://huggingface.co/tiiuae/falcon-180b), to illustrate the impact (and limitations) of finetuning on a dataset of conversations and instructions. We encourage the community to further build upon the base model, and to create even better instruct/chat versions!
                
#                 ⚠️ **Limitations**: the model can and will produce factually incorrect information, hallucinating facts and actions. As it has not undergone any advanced tuning/alignment, it can produce problematic outputs, especially if prompted to do so. Finally, this demo is limited to a session length of about 1,000 words.
#                 """
#             )

#     gr.ChatInterface(
#         generate, 
#         examples=EXAMPLES,
#         additional_inputs=additional_inputs,
#     ) 

#demo.launch(show_api=True, share=True)
#demo.queue(concurrency_count=100, api_open=False).launch(show_api=True)

def query(system_prompt, user_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
    print(temperature, max_new_tokens, top_p, repetition_penalty)
    seed = 42
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        stop_sequences=STOP_SEQUENCES,
        do_sample=True,
        seed=seed,
    )
    prompt = f"System: {system_prompt}\nUser: {user_prompt}\n"
    print(prompt)
    print('-----')
    #output = client.text_generation(prompt, **generate_kwargs, details=True, return_full_text=False)
    #print(output)
    stream = client.text_generation(prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""
    for response in stream:
        output += response.token.text

        for stop_str in STOP_SEQUENCES:
            if output.endswith(stop_str):
                output = output[:-len(stop_str)]
                output = output.rstrip()
                #yield output
        #yield output
    print(output)
    return output

iface = gr.Interface(
    query,
    inputs=["text","text","text","text","text","text"],
    outputs="text",
)


iface.queue()
iface.launch()