import json
from huggingface_hub import InferenceClient
import gradio as gr
import PyPDF2
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)
    text = ' '.join(df['Unnamed: 1'].astype(str))
    sentences = text.split('.')
    sentences = [s.strip() for s in sentences if s.strip() and s.strip() != 'nan']
    return sentences

def save_to_json(data, filename="synthetic_data.json"):
    with open(filename, mode='a', encoding='utf-8') as file:
        json.dump(data, file, indent=4, ensure_ascii=False)

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

    with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.json') as tmp:
        for sentence in sentences:
            sentence = sentence.strip()
            if not sentence:
                continue

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

            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}}", **generate_kwargs, stream=True, details=True, return_full_text=False)
                output = ""
                for response in stream:
                    output += response.token.text

                try:
                    json_output = json.loads(output)
                    data.append({"original_sentence": sentence, "generated_data": json_output})
                except json.JSONDecodeError:
                    print(f"Error decoding JSON for sentence '{sentence}': {output}")

            except Exception as e:
                print(f"Error generating data for sentence '{sentence}': {e}")

        save_to_json(data, tmp.name)
        tmp_path = tmp.name

    return tmp_path

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()