app.py

import gradio as gr
import pandas as pd
import requests
from transformers import GPT2LMHeadModel, GPT2Tokenizer, LlamaTokenizer, pipeline
from huggingface_hub import HfFolder
from io import StringIO
import os
from flask import Flask, request, jsonify

hf_token = os.getenv('HF_API_TOKEN')

if not hf_token:
    raise ValueError("Hugging Face API token is not set. Please set the HF_API_TOKEN environment variable.")

# Ensure that you load the token before making API requests

# Set environment variable to avoid floating-point errors
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'

# Load the tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model_gpt2 = GPT2LMHeadModel.from_pretrained('gpt2')

# Create a pipeline for text generation using GPT-2
text_generator = pipeline("text-generation", model=model_gpt2, tokenizer=tokenizer)

# Loading LLama3.1 tokenizer
try:
    tokenizer_llama = LlamaTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B")
except OSError as e:
    print(f"Error loading tokenizer: {e}")

# Define your prompt template
prompt_template = """\
You are an expert in generating synthetic data for machine learning models.
Your task is to generate a synthetic tabular dataset based on the description provided below.
Description: {description}
The dataset should include the following columns: {columns}
Please provide the data in CSV format with a minimum of 100 rows per generation.
Ensure that the data is realistic, does not contain any duplicate rows, and follows any specific conditions mentioned.
Example Description:
Generate a dataset for predicting house prices with columns: 'Size', 'Location', 'Number of Bedrooms', 'Price'
Example Output:
Size,Location,Number of Bedrooms,Price
1200,Suburban,3,250000
900,Urban,2,200000
1500,Rural,4,300000
...
Description:
{description}
Columns:
{columns}
Output: """

def preprocess_user_prompt(user_prompt):
    generated_text = text_generator(user_prompt, max_length=60, num_return_sequences=1)[0]["generated_text"]
    return generated_text

def format_prompt(description, columns):
    processed_description = preprocess_user_prompt(description)
    prompt = prompt_template.format(description=processed_description, columns=",".join(columns))
    return prompt

API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3.1-8B"

generation_params = {
    "top_p": 0.90,
    "temperature": 0.8,
    "max_new_tokens": 512,
    "return_full_text": False,
    "use_cache": False
}

def generate_synthetic_data(description, columns):
    try:
        formatted_prompt = format_prompt(description, columns)
        payload = {"inputs": formatted_prompt, "parameters": generation_params}
        headers = {"Authorization": f"Bearer {hf_token}"}
        response = requests.post(API_URL, headers=headers, json=payload)
        
        if response.status_code == 200:
            response_json = response.json()
            if isinstance(response_json, list) and len(response_json) > 0 and "generated_text" in response_json[0]:
                return response_json[0]["generated_text"]
            else:
                raise ValueError("Unexpected response format or missing 'generated_text' key")
        else:
            print(f"Error details: {response.text}") 
            raise ValueError(f"API request failed with status code {response.status_code}: {response.text}")
    except Exception as e:
        print(f"Error in generate_synthetic_data: {e}")
        return f"Error: {e}"

def generate_large_synthetic_data(description, columns, num_rows=1000, rows_per_generation=100):
    data_frames = []
    num_iterations = num_rows // rows_per_generation

    for _ in range(num_iterations):
        generated_data = generate_synthetic_data(description, columns)
        if "Error" in generated_data:
            return generated_data
        df_synthetic = process_generated_data(generated_data)
        data_frames.append(df_synthetic)
    
    return pd.concat(data_frames, ignore_index=True)

def process_generated_data(csv_data):
    data = StringIO(csv_data)
    df = pd.read_csv(data)
    return df

def main(description, columns):
    description = description.strip()
    columns = [col.strip() for col in columns.split(',')]
    df_synthetic = generate_large_synthetic_data(description, columns)
    if isinstance(df_synthetic, str) and "Error" in df_synthetic:
        return df_synthetic  # Return the error message if any
    return df_synthetic.to_csv(index=False)

# Gradio interface
iface = gr.Interface(
    fn=main,
    inputs=[
        gr.Textbox(label="Description", placeholder="e.g., Generate a dataset for predicting students' grades"),
        gr.Textbox(label="Columns (comma-separated)", placeholder="e.g., name, age, course, grade")
    ],
    outputs="text",
    title="Synthetic Data Generator",
    description="Generate synthetic tabular datasets based on a description and specified columns."
)

# Run the Gradio app
iface.launch()