app.py

import gradio as gr
from huggingface_hub import InferenceClient

client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    file=None
):
    # Initialize the messages with the system message
    messages = [{"role": "system", "content": system_message}]

    # Read file content if a file is uploaded
    if file is not None:
        try:
            if hasattr(file, 'value'):  # Check if file is a NamedString or similar
                file_content = file.value.decode("utf-8")  # Decode the string content
            else:
                file_content = file.read().decode("utf-8")  # Fallback for other file types

            print("File content:", file_content)  # Debug print
            message = f"{file_content}\n\n{message}"  # Append file content to message
        except Exception as e:
            print("Error reading file:", e)
            message = f"(Error reading file: {e})\n\n{message}"

    # Append conversation history
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    # Append the latest user message
    messages.append({"role": "user", "content": message})

    response = ""

    # Stream response from the model
    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

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=32000, value=2048, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=1.0, 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(label="Upload a text file", file_types=[".txt"])
    ],
)

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