from flask import Flask, request, jsonify
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import gradio as gr
from huggingface_hub import InferenceClient

app = Flask(__name__)

tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6")
model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-cnn-12-6")

"""
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


@app.route('/summarize', methods=['POST'])
def summarize():
    data = request.json
    text = data.get('text', '')
    if not text:
        return jsonify({'error': 'No text provided'}), 400

    inputs = tokenizer([text], max_length=1024, return_tensors="pt", truncation=True)
    summary_ids = model.generate(inputs["input_ids"], max_length=60, min_length=10, length_penalty=2.0, num_beams=4, early_stopping=True)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return jsonify({'summary': summary})

"""
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)",
        ),
    ],
)

if __name__ == '__main__':
    from waitress import serve
    serve(app, host='0.0.0.0', port=5005)
    demo.launch()