main.py

from fastapi import FastAPI, Request
from pydantic import BaseModel
import transformers
import torch
from fastapi.middleware.cors import CORSMiddleware


import os
access_token_read = os.getenv('DS4')
print(access_token_read)

from huggingface_hub import login
login(token = access_token_read)

# Define the FastAPI app
app = FastAPI()

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

# Load the model and tokenizer from Hugging Face
model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"  # Replace with an appropriate model
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
model = transformers.AutoModelForCausalLM.from_pretrained(
    model_id, device_map="auto", torch_dtype=torch.bfloat16
)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=150,
    temperature=0.7,
    device_map="auto",
)

# Define the request model for email input
class EmailRequest(BaseModel):
    subject: str
    sender: str
    recipients: str
    body: str

# Define the FastAPI endpoint for email summarization
@app.post("/summarize-email/")
async def summarize_email(email: EmailRequest):
    prompt = create_email_prompt(email.subject, email.sender, email.recipients, email.body)
    
    # Use the pipeline to generate the summary
    summary = pipeline(prompt)[0]["generated_text"]
    
    return {"summary": summary}