app.py

from fastapi import FastAPI, File, UploadFile
from pydantic import BaseModel
from typing import List
from pathlib import Path
import shutil
import tempfile
import os
import uuid

from langchain_docling import DoclingLoader
from langchain_docling.loader import ExportType


app = FastAPI()

resumes = []
jobs = []

UPLOAD_DIR = Path("uploads")
UPLOAD_DIR.mkdir(exist_ok=True)

@app.post("/upload")
async def upload_file(file: UploadFile = File(...)):
    # print(file)
    # file_path = Path(file.filename)
    # with file_path.open("wb") as buffer:
    #     shutil.copyfileobj(file.file, buffer)

    # with tempfile.NamedTemporaryFile(delete=False, suffix=file.filename) as temp_file:
    #     # Efficiently write the uploaded file's content to the temporary file
    #     contents = await file.read()
    #     temp_file.write(contents)
            
    # temp_file_path = temp_file.name 

    suffix = os.path.splitext(file.filename)[-1] or ".pdf"
    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix, dir="/tmp") as tmp:
        shutil.copyfileobj(file.file, tmp)
        tmp_path = tmp.name

    # At this point, tmp_path is a real file path in /tmp
    # Debug: check if file is valid
    size = os.path.getsize(tmp_path)
    print(f"Saved {file.filename} -> {tmp_path} ({size} bytes)")

    print("[TMP PATH]", str(tmp_path))
    
    loader = DoclingLoader(file_path="" + str(tmp_path), export_type=ExportType.MARKDOWN)
    docs = loader.load()
    # docs = docs.model_dump()
    result = docs[0].model_dump()
    result["id"] = str(uuid.uuid4())
    jobs.append(result)
    return {
        "code":201,
        "message":"Request was successful.",
        "data": result
    }



app.get("/jobs")
def read_root():
    return {
        "code":200,
        "message":"Request was successful."
        "data": jobs
    }


# class InputResume(BaseModel):
#     content: str
    


# @app.post("/suggest/")
# async def suggestion(data: InputResume):
#     return {
#         "code":201,
#         "message":"Request was successful.",
#         "data": InputResume.model_dump_json()
#     }





from ranker import rank_resume 
from embeddings import rank_jobs

# Function to wrap the existing rank_resume
def process_input(job_description, resumes):
    print("[JOB DESC]", job_description)
    print("[RESUMES]", resumes)
    resumes = [r for r in resumes if r and r.strip() != ""]  # Remove empty
    if not job_description.strip() or not resumes:
        return "Please provide both job description and at least one resume."
    
    return rank_resume(job_description, resumes)[1]


def process_input_suggestion(resume, job_descriptions):
    # print("[JOB DESC]", job_description)
    # print("[RESUMES]", resumes)
    # resumes = [r for r in resumes if r and r.strip() != ""]  # Remove empty
    # if not job_description.strip() or not resumes:
    #     return "Please provide both resume and at least one job description."

    return rank_jobs(job_descriptions, resume)[1]
    
    # results = zip(*rank_jobs(resumes, job_description))
    # formatted_output = ""
    # for i, (resume, score) in enumerate(results, 1):
    #     formatted_output += f"Job #{i}:\nScore: {score:.2f}\nJob Description Snippet: {resume[:200]}...\n\n-------\n\n"
    # return formatted_output


    
app.get("/")
def read_root():
    return {"message": "Hello, World!"}


class InputData(BaseModel):
    resumes: List[str]
    job_description: str

class InputData2(BaseModel):
    job_descriptions: List[str]
    resume: str

@app.post("/rank/")
async def process_data(data: InputData):
    return dict(scores=process_input(data.job_description, data.resumes))


@app.post("/suggest/")
async def suggestion(data: InputData2):
    return {
        "scores":process_input_suggestion(data.resume, data.job_descriptions)
    }