import json
from huggingface_hub import InferenceClient
import gradio as gr
import random
import pandas as pd
from io import BytesIO
import csv
import os
import io
import tempfile
import re


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

def extract_sentences_from_excel(file):
    df = pd.read_excel(file)
    sentences = df['metn'].astype(str).tolist()
    return sentences

def generate(file, prompt, temperature, max_new_tokens, top_p, repetition_penalty):
    sentences = extract_sentences_from_excel(file)
    data = []

    generate_kwargs = {
        "temperature": temperature,
        "max_new_tokens": max_new_tokens,
        "top_p": top_p,
        "repetition_penalty": repetition_penalty,
        "do_sample": True,
        "seed": 42,
    }

    for sentence in sentences:
        try:
            stream = client.text_generation(f"{prompt} Output the response in the following JSON format: {{'generated_sentence': 'The generated sentence text', 'confidence_score': 0.9}} {sentence}", **generate_kwargs, stream=True, details=True, return_full_text=False)
            output = ""
            for response in stream:
                output += response.token.text
            data.append({"original_sentence": sentence, "generated_data": output})
        except Exception as e:
            print(f"Error generating data for sentence '{sentence}': {e}")

    filename = "synthetic_data.json"
    save_to_json(data, filename)

    return filename

def save_to_json(data, filename):
    json_data = []
    for item in data:
        generated_sentences = []
        confidence_scores = []
        for match in re.finditer(r"{'generated_sentence': '(.+?)', 'confidence_score': ([\d\.]+)}", item['generated_data']):
            generated_sentences.append(match.group(1))
            confidence_scores.append(float(match.group(2)))
        json_data.append({
            'original_sentence': item['original_sentence'],
            'generated_sentences': generated_sentences,
            'confidence_scores': confidence_scores
        })

    with open(filename, mode='w', encoding='utf-8') as file:
        json.dump(json_data, file, indent=4, ensure_ascii=False)

# Gradio arayüzü
gr.Interface(
    fn=generate,
    inputs=[
        gr.File(label="Upload Excel File", file_count="single", file_types=[".xlsx"]),
        gr.Textbox(label="Prompt", placeholder="Enter your prompt here"),
        gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs"),
        gr.Slider(label="Max new tokens", value=256, minimum=0, maximum=5120, step=64, interactive=True, info="The maximum numbers of new tokens"),
        gr.Slider(label="Top-p (nucleus sampling)", value=0.95, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens"),
        gr.Slider(label="Repetition penalty", value=1.0, minimum=1.0, maximum=2.0, step=0.1, interactive=True, info="Penalize repeated tokens"),
    ],
    outputs=gr.File(label="Synthetic Data"),
    title="SDG",
    description=" *AYE* QABIL.",
    allow_flagging="never",
).launch()