import gradio as gr
from huggingface_hub import InferenceClient
import PyPDF2
import io

"""
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("HuggingFaceH4/zephyr-7b-beta")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    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


def extract_text_from_pdf(pdf_file):
    if pdf_file is None:
        return "No file uploaded."
    
    try:
        pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_file))
        text = ""
        for page in pdf_reader.pages:
            text += page.extract_text() + "\n\n"
        return text.strip()
    except Exception as e:
        return f"An error occurred: {str(e)}"


"""
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.Textbox(value="You are a friendly Chatbot.", label="System message"),
        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)",
        ),
    ],
)

pdf_interface = gr.Interface(
    fn=extract_text_from_pdf,
    inputs=gr.File(label="Upload PDF", type="binary"),
    outputs="text",
    title="PDF Text Extractor",
    description="Upload a PDF file to extract its text content."
)

demo = gr.TabbedInterface(
    [demo, pdf_interface],
    ["Chat", "PDF Extractor"]
)

if __name__ == "__main__":
    demo.launch()