backend/main.py

import os
import json

from fastapi import FastAPI, HTTPException, Query
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
import openai
from dotenv import load_dotenv
from typing import List

# Load environment variables
load_dotenv()

# Initialize OpenAI client
client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY'))

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["http://localhost:3000"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

class Message(BaseModel):
    content: str
    role: str

class ChatRequest(BaseModel):
    messages: List[Message]

async def stream_text(messages: List[Message]):
    try:
        formatted_messages = [
            {"role": "system", "content": """You are an AI learning assistant for PlayGo AI, 
             an educational platform. Your goal is to help students learn and understand various 
             subjects. Provide clear, concise, and accurate explanations."""},
        ] + [{"role": msg.role, "content": msg.content} for msg in messages]

        stream = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=formatted_messages,
            temperature=0.7,
            stream=True
        )

        for chunk in stream:
            for choice in chunk.choices:
                if choice.finish_reason == "stop":
                    continue

                else:
                    yield '0:{text}\n'.format(text=json.dumps(choice.delta.content))

            if chunk.choices == []:
                usage = chunk.usage
                prompt_tokens = usage.prompt_tokens
                completion_tokens = usage.completion_tokens
                yield 'd:{{"finishReason":"{reason}","usage":{{"promptTokens":{prompt},"completionTokens":{completion}}}}}\n'.format(
                    reason="stop",
                    prompt=prompt_tokens,
                    completion=completion_tokens
                )

    except Exception as e:
        print(f"Error in stream_text: {str(e)}")
        yield f"Error: {str(e)}".encode('utf-8')

@app.post("/api/landing_page_chat")
async def landing_page_chat(request: ChatRequest):
    response = StreamingResponse(
        stream_text(request.messages),
    )
    response.headers['x-vercel-ai-data-stream'] = 'v1'
    return response

@app.get("/api/hello")
async def root():
    return {"message": "Hello, World!"}