from huggingface_hub import InferenceClient
import gradio as gr
import random
from langchain_community.tools import DuckDuckGoSearchRun

API_URL = "https://api-inference.huggingface.co/models/"
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")

# Initialize DuckDuckGo search tool
duckduckgo_search = DuckDuckGoSearchRun()

def format_prompt(message, history):
    prompt = "<s>"
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt

def generate(prompt, history, temperature=0.9, max_new_tokens=512, top_p=0.95, repetition_penalty=1.0):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=random.randint(0, 10**7),
    )

    formatted_prompt = format_prompt(prompt, history)

    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
        # Yield model's response first
        yield output
        
    # Now, perform DuckDuckGo search and yield results
    search_result = duckduckgo_search.run(prompt)
    if search_result:
        yield search_result
    else:
        yield "Sorry, I couldn't find any relevant information."

additional_inputs=[
    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=512,
        minimum=64,
        maximum=1024,
        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",
    )
]

customCSS = """
#component-7 { # this is the default element ID of the chat component
  height: 800px; # adjust the height as needed
  flex-grow: 1;
}
"""

with gr.Blocks(css=customCSS) as demo:
    gr.ChatInterface(
        generate,
        title = "RAG_FRIDAY_3.0🤖 WELCOME TO OPEN-SOURCE FREEDOM🤗",
        description = "Getting real-time updated results for prompts is still propreitary in face of GPT-4,Co-Pilot etc. This app serves as a open-source alternative for this! UPDATE: Previous version of this app i.e. RAG_FRIDAY_mark_2 has faced some techncial issues due to rate limit errors. Problem and solution have been updated by me thanks to this community thread: https://github.com/joaomdmoura/crewAI/issues/136",
        additional_inputs=additional_inputs,
    )

demo.queue().launch(debug=True)