app3.py

from fastapi import FastAPI, Request, Form
from fastapi.templating import Jinja2Templates
import csv
from datetime import datetime  # Used for generating unique filenames
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Install these libraries if you haven't already:
# pip install transformers accelerate

app = FastAPI()
templates = Jinja2Templates(directory="templates")

# Load GPT-J 6B model and tokenizer
model_name = "EleutherAI/gpt-j-6B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

async def generate_conversation(prompt):
    try:
        # Tokenize the prompt
        inputs = tokenizer(prompt, return_tensors="pt")

        # Generate response using local model
        output = model.generate(**inputs)
        conversation = tokenizer.decode(output[0], skip_special_tokens=True)

        return conversation
    except Exception as e:
        return f"Error: {str(e)}"

def save_to_csv(prompt, conversation):
    timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
    filename = f"info.csv"

    with open(filename, mode='w', newline='', encoding='utf-8') as csv_file:
        csv_writer = csv.writer(csv_file)
        csv_writer.writerow(['Prompt', 'Generated Conversation'])
        csv_writer.writerow([prompt, conversation])

    return filename

@app.get("/")
def read_form(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})

@app.post("/")
async def generate_and_display(request: Request, prompt: str = Form(...)):
    conversation = await generate_conversation(prompt)
    csv_filename = save_to_csv(prompt, conversation)
    return templates.TemplateResponse("index.html", {"request": request, "prompt": prompt, "conversation": conversation, "csv_filename": csv_filename})

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="127.0.0.1", port=8000)