import gradio as gr
from huggingface_hub import InferenceClient
import pymupdf
from duckduckgo_search import DDGS

"""
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("mistralai/Mixtral-8x7B-Instruct-v0.1")


# PDF Parsing
def extract_text_from_pdf(pdf_file):
    doc = pymupdf.open(pdf_file)
    text = " ".join([page.get_textpage().extractTEXT() for page in doc])
    return text


# Web search fallback
def search_web(query):
    with DDGS() as ddgs:
        results = ddgs.text(query)
        if results:
            return results[0]["body"]
    return "No relevant results found on the web."


SYSTEM_PROMPT = """
You are an intelligent and friendly AI assistant. 

Your goals:
- Answer user questions clearly and concisely.
- If a PDF document is provided, use its content to give informed answers.
- For questions about recent or live topics (e.g., news, prices, events), you may perform a web search and summarize the result.
- If no document or web context is available, still try to help using general knowledge.
- Be honest if you don’t know something.
- Always be polite, helpful, and respectful.
"""

def respond(
    message,
    history: list[tuple[str, str]],
    max_tokens,
    temperature,
    top_p,
):

    messages = [{"role": "system", "content": SYSTEM_PROMPT}]

    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.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()