from huggingface_hub import InferenceClient
import gradio as gr
import random
import pandas as pd
from io import BytesIO

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

def extract_text_from_excel(file):
    df = pd.read_excel(file)
    text = ' '.join(df['data'].astype(str))
    return text

def generate_sentences(text, temperature, max_new_tokens, top_p, repetition_penalty):
    sentences = text.split('.')
    random.shuffle(sentences)  # Shuffle sentences

    generated_data = []

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

            generated_sentences = [s.strip() for s in output.split('.') if s.strip()]
            generated_data.extend([(sentence, generated_sentence) for generated_sentence in generated_sentences])

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

    return generated_data

def save_to_csv(data, filename="synthetic_data.csv"):
    with open(filename, mode='w', newline='', encoding='utf-8') as file:
        writer = csv.writer(file)
        writer.writerow(['Original Sentence', 'Generated Sentence'])
        writer.writerows(data)

def generate(file, temperature, max_new_tokens, top_p, repetition_penalty):
    text = extract_text_from_excel(file)
    data = generate_sentences(text, temperature, max_new_tokens, top_p, repetition_penalty)
    save_to_csv(data)
    return gr.File.update(value=filename, visible=True)

gr.Interface(
    fn=generate,
    inputs=[
        gr.File(label="Upload Excel File", file_count="single", file_types=[".xlsx"]),
        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()