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}"},
)
###################  mY code ############################
def generate_headlines(topic):
    prompt = f"Create at most 5 headlines that highlight {topic}. The headlines should be concise, attention-grabbing, and suitable for use in a news video."
    sequences = generate(
        prompt,
        history=[],
        system_prompt="",
        temperature=0.9,
        max_new_tokens=256,
        top_p=0.95,
        repetition_penalty=1.0,
    )
    headlines = [seq for seq in sequences]
    return "\n".join(headlines)

######################################################
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",
    )
]

iface_1 = gr.Interface(
    fn=generate_headlines,
    inputs=gr.components.Textbox(placeholder="Enter the topic"),
    outputs="text",
    examples=EXAMPLES
)

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

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=4):  # Adjusted the 'scale' parameter to be an integer
            gr.Image("better_banner.jpeg", elem_id="banner-image", show_label=False)
        with gr.Column(scale=6):
            gr.Markdown(
                """# Falcon-180B Demo
                ... [rest of the markdown content] ...
                """
            )

demo = gr.TabbedInterface([iface_1, main_i], ["Get headlines", "Chatbot Demo"])
demo.queue(concurrency_count=100, api_open=False).launch(show_api=False)