app.py

from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse, JSONResponse
from pydantic import BaseModel
import pandas as pd
import os
import requests
from transformers import GPT2LMHeadModel, GPT2Tokenizer, AutoTokenizer, pipeline
from io import StringIO
from fastapi.middleware.cors import CORSMiddleware
from huggingface_hub import HfFolder
from tqdm import tqdm

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

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.")


# Load GPT-2 model and tokenizer
tokenizer_gpt2 = AutoTokenizer.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_gpt2)

# Define prompt template for generating the dataset
prompt_template = """\
You are an AI specialized in generating synthetic tabular data specifically for machine learning purposes.

Task: Generate a synthetic dataset based on the provided description and column names.

Description: {description}

Columns: {columns}

Instructions:

Output only the tabular data in valid CSV format.
Include the header row followed by the data rows.
Do not generate any additional text, explanations, comments, or code.
Ensure that the values for each column are contextually appropriate.

Format Example (do not include this line or the following example in your output):
Column1,Column2,Column3
Value1,Value2,Value3
Value4,Value5,Value6
"""


# Define generation parameters
generation_params = {
    "top_p": 0.90,
    "temperature": 0.8,
    "max_new_tokens": 1024,
    "return_full_text": False,
    "use_cache": False
}

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

def generate_synthetic_data(description, columns):
    formatted_prompt = format_prompt(description, columns)
    payload = {"inputs": formatted_prompt, "parameters": generation_params}

    # Call Mixtral model to generate data
    response = requests.post("https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1", 
                             headers={"Authorization": f"Bearer {hf_token}"}, json=payload)
    
    if response.status_code == 200:
        return response.json()[0]["generated_text"]
    else:
        print(f"Error generating data: {response.status_code}, {response.text}")
        return None

def process_generated_data(csv_data):
    try:
        # Ensure the data is cleaned and correctly formatted
        cleaned_data = csv_data.replace('\r\n', '\n').replace('\r', '\n')
        data = StringIO(cleaned_data)

        # Read the CSV data with specific parameters to handle irregularities
        df = pd.read_csv(data)

        return df
    except pd.errors.ParserError as e:
        print(f"Failed to parse CSV data: {e}")
        return None

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

    for _ in tqdm(range(num_rows // rows_per_generation), desc="Generating Data"):
        generated_data = generate_synthetic_data(description, columns)
        
        if generated_data:
           
            df_synthetic = process_generated_data(generated_data)

            if df_synthetic is not None and not df_synthetic.empty:
                data_frames.append(df_synthetic)
            else:
                print("Skipping invalid generation.")

    if data_frames:
        return pd.concat(data_frames, ignore_index=True)
    else:
        print("No valid data frames to concatenate.")
        return pd.DataFrame(columns=columns)

class DataGenerationRequest(BaseModel):
    description: str
    columns: list[str]

@app.post("/generate/")
def generate_data(request: DataGenerationRequest):
    description = request.description.strip()
    columns = [col.strip() for col in request.columns]
    csv_data = generate_large_synthetic_data(description, columns, num_rows=1000, rows_per_generation=100)
    
    if csv_data.empty:
        return JSONResponse(content={"error": "No valid data generated"}, status_code=500)
    
    csv_buffer = StringIO()
    csv_data.to_csv(csv_buffer, index=False)
    csv_buffer.seek(0)

    return StreamingResponse(
        csv_buffer,
        media_type="text/csv",
        headers={"Content-Disposition": "attachment; filename=generated_data.csv"}
    )

@app.get("/")
def greet_json():
    return {"Hello": "World!"}