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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)
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