import gradio as gr
from huggingface_hub import InferenceClient

# Existing client setup for chat model
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Function to process file uploads (e.g., text or PDF files)
def analyze_file(file):
    if file is None:
        return "Please upload a file."

    # Assuming a text file for simplicity, modify for other types as needed (e.g., PDF)
    try:
        with open(file.name, "r", encoding="utf-8") as f:
            content = f.read()
        return content
    except Exception as e:
        return f"Error reading file: {str(e)}"

# Existing chatbot response function
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

# Adding a new function that uses file content as input to the chatbot
def respond_with_file(file, history, system_message, max_tokens, temperature, top_p):
    file_content = analyze_file(file)  # Get the content from the file
    if isinstance(file_content, str):
        return respond(file_content, history, system_message, max_tokens, temperature, top_p)
    else:
        return file_content

# Gradio interface setup
demo = gr.ChatInterface(
    respond,  # Main chat function
    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)"),
        gr.File(file_types=["text"], label="Upload a file (optional)"),
    ]
)

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