app.py

import os
from openai import OpenAI  # Use only this import
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import pipeline
from keybert import KeyBERT  # For BERT-based keyword extraction

app = FastAPI()

# Load BERT model for key point extraction
kw_model = KeyBERT()

# Get OpenAI API key from environment variable
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    raise ValueError("OpenAI API key is missing. Set OPENAI_API_KEY as an environment variable.")

# Initialize OpenAI client
client = OpenAI(api_key=OPENAI_API_KEY)

# Define request format
class SummarizationRequest(BaseModel):
    text: str

@app.post("/summarize")
def summarize_text(request: SummarizationRequest):
    if not request.text.strip():
        raise HTTPException(status_code=400, detail="No text provided")

    # Step 1: Extract key points using BERT (KeyBERT)
    key_points = kw_model.extract_keywords(request.text, keyphrase_ngram_range=(1, 2), stop_words='english', top_n=5)
    extracted_points = ", ".join([kp[0] for kp in key_points])

    # Step 2: Generate summary using GPT-4
    try:
        response = client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are an AI assistant that summarizes text."},
                {"role": "user", "content": f"Summarize the following text in a concise way:\n\n{request.text}"}
            ]
        )
        summary = response.choices[0].message.content  # Correct response parsing

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error with GPT-4 API: {str(e)}")

    return {
        "key_points": extracted_points,
        "summary": summary
    }

@app.get("/")
def greet_json():
    return {"message": "Welcome to the AI Summarizer API!"}