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=["*"],  # You can specify domains here
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Access the Hugging Face API token from environment variables
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 = 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_gpt2)

def preprocess_user_prompt(user_prompt):
    # Generate a structured prompt based on the user input
    generated_text = text_generator(user_prompt, max_length=50, num_return_sequences=1)[0]["generated_text"]
    return generated_text

# Define 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.
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: """

tokenizer_mixtral = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", token=hf_token)

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/mistralai/Mixtral-8x7B-Instruct-v0.1"

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):
    formatted_prompt = format_prompt(description, columns)
    payload = {"inputs": formatted_prompt, "parameters": generation_params}
    try:
        response = requests.post(API_URL, headers={"Authorization": f"Bearer {hf_token}"}, json=payload)
        response.raise_for_status()
        data = response.json()
        if 'generated_text' in data[0]:
            return data[0]['generated_text']
        else:
            raise ValueError("Invalid response format from Hugging Face API.")
    except (requests.RequestException, ValueError) as e:
        print(f"Error during API request or response processing: {e}")
        return ""

def process_generated_data(csv_data, expected_columns):
    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
        df = pd.read_csv(data, delimiter=',')
        
        # Check if the DataFrame has the expected columns
        if set(df.columns) != set(expected_columns):
            print(f"Unexpected columns in the generated data: {df.columns}")
            return None
        
        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, columns)
            if df_synthetic is not None and not df_synthetic.empty:
                data_frames.append(df_synthetic)
            else:
                print("Skipping invalid generation.")
        else:
            print("Skipping empty or 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)
    
    # Convert the DataFrame to CSV format
    csv_buffer = StringIO()
    csv_data.to_csv(csv_buffer, index=False)
    csv_buffer.seek(0)

    # Return the CSV data as a downloadable file
    return StreamingResponse(
        csv_buffer,
        media_type="text/csv",
        headers={"Content-Disposition": "attachment; filename=generated_data.csv"}
    )

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