File size: 2,073 Bytes
64c1f09 1ea6737 27367c2 1ea6737 9959186 64c1f09 9959186 cb35691 9959186 64c1f09 9959186 1ea6737 64c1f09 27367c2 1ea6737 27367c2 64c1f09 27367c2 0a7206e 64c1f09 27367c2 1ea6737 27367c2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
from fastapi import FastAPI
from pydantic import BaseModel
import torch
from transformers import pipeline
from fastapi.middleware.cors import CORSMiddleware
import os
from huggingface_hub import login
# Get access token from environment variable
access_token_read = os.getenv('DS4')
print(access_token_read)
# Login to Hugging Face Hub
login(token=access_token_read)
# Define the FastAPI app
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")
# Define the request model for email input
class EmailRequest(BaseModel):
subject: str
sender: str
recipients: str
body: str
def create_email_prompt(subject, sender, recipients, body):
messages = [
{
"role": "system",
"content": "You are an email summarizer. Your goal is to provide a concise summary by focusing on key points, action items, and urgency."
},
{
"role": "user",
"content": f"""
Summarize the following email by focusing on the key points, action items, and urgency.
Email Details:
Subject: {subject}
Sender: {sender}
Recipients: {recipients}
Body:
{body}
Provide a concise summary that includes important information, if any actions are required, and the priority of the email.
"""
}
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
return prompt
# 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}
|