main.py

from fastapi import FastAPI
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from langchain_community.chat_models import ChatOpenAI
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import ChatPromptTemplate
from dotenv import load_dotenv
import os

# Load environment variables from .env file
load_dotenv()

# Initialize FastAPI app
app = FastAPI()

# Enable CORS for frontend access
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Request schema
class FeedbackRequest(BaseModel):
    exam_type: str
    essay_text: str

# Define the output schema for structured feedback
response_schemas = [
    ResponseSchema(name="logic", description="Feedback on logic and relevance"),
    ResponseSchema(name="grammar", description="Feedback on grammar and writing style"),
    ResponseSchema(name="score", description="Overall score out of 10"),
    ResponseSchema(name="suggestions", description="Suggestions for improvement")
]

parser = StructuredOutputParser.from_response_schemas(response_schemas)

# Define the prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an English examiner. Provide detailed structured feedback on the student's essay."),
    ("user", "Essay Type: {exam_type}\nEssay: {essay_text}\n{format_instructions}")
])

# Initialize OpenAI model using API key from environment
model = ChatOpenAI(
    openai_api_key=os.getenv("OPENAI_API_KEY"),
    model="gpt-4o-mini",
    temperature=0.3
)

# Feedback endpoint
@app.post("/feedback")
async def get_feedback(req: FeedbackRequest):
    try:
        formatted_prompt = prompt.format(
            exam_type=req.exam_type,
            essay_text=req.essay_text,
            format_instructions=parser.get_format_instructions()
        )

        # Get model response and parse structured output
        response = model.invoke(formatted_prompt)
        output = parser.parse(response.content)
        return output

    except Exception as e:
        return {"error": str(e)}



@app.get("/")
async def root():
    return {"message": "English Writting Analyst is running 🎯"}

if __name__ == "__main__":
    uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=True)