Initial deploy: ATS Score Analyzer API

app.py

@@ -2,7 +2,8 @@ import os
 import time
 from fastapi import FastAPI, HTTPException, Request
 from pydantic import BaseModel
-from ats_core import ats_score
+from ats_core import ats_score, extract_text_from_pdf, ROLE_TEMPLATES
+
 
 PORT = int(os.environ.get("PORT", 7860))
 
@@ -33,11 +34,39 @@ def check_rate_limit(request: Request):
     usage_tracker[ip][today] = usage_tracker[ip].get(today, 0) + 1
 
 
+from fastapi import UploadFile, File, Form
+
 @app.post("/ats-score")
-def compute_ats(req: ATSRequest, request: Request):
+async def compute_ats(
+    resume_file: UploadFile = File(...),
+    job_description: str = Form(""),
+    role: str = Form(""),
+    request: Request = None
+):
     check_rate_limit(request)
-    return ats_score(req.resume_text, req.job_description)
 
+    # Read resume PDF
+    if resume_file.content_type != "application/pdf":
+        raise HTTPException(status_code=400, detail="Resume must be a PDF")
+
+    file_bytes = await resume_file.read()
+    resume_text = extract_text_from_pdf(file_bytes)
+
+    if not resume_text:
+        raise HTTPException(status_code=400, detail="Could not extract text from resume")
+
+    # Decide JD source
+    if job_description.strip():
+        jd_text = job_description
+    elif role.lower() in ROLE_TEMPLATES:
+        jd_text = ROLE_TEMPLATES[role.lower()]
+    else:
+        raise HTTPException(
+            status_code=400,
+            detail="Provide job description text or select a valid role"
+        )
+
+    return ats_score(resume_text, jd_text)
 
 @app.get("/health")
 def health():

ats_core.py

@@ -2,10 +2,31 @@ from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 import nltk
 import re
+import pdfplumber
 
 nltk.download("stopwords")
 from nltk.corpus import stopwords
 
+ROLE_TEMPLATES = {
+    "backend": """
+    Backend Engineer with experience in Python, APIs, databases,
+    system design, REST services, Docker, and scalable backend systems.
+    """,
+    "frontend": """
+    Frontend Developer skilled in JavaScript, React, HTML, CSS,
+    responsive design, UI/UX, and modern frontend frameworks.
+    """,
+    "ml": """
+    Machine Learning Engineer with experience in Python, data analysis,
+    machine learning models, feature engineering, evaluation metrics,
+    and deployment of ML systems.
+    """,
+    "data": """
+    Data Analyst with experience in SQL, Python, data visualization,
+    statistics, dashboards, and business insights.
+    """
+}
+
 model = SentenceTransformer("all-MiniLM-L6-v2")
 STOPWORDS = set(stopwords.words("english"))
 
@@ -80,3 +101,11 @@ def ats_score(resume_text, jd_text):
         "formatting_score": to_float(round(format_score * 10, 2)),
         "missing_keywords": list(set(jd_keywords) - set(resume_keywords))[:10]
     }
+
+def extract_text_from_pdf(file_bytes):
+    text = ""
+    with pdfplumber.open(file_bytes) as pdf:
+        for page in pdf.pages:
+            if page.extract_text():
+                text += page.extract_text() + "\n"
+    return text.strip()

requirements.txt

@@ -4,3 +4,5 @@ sentence-transformers
 scikit-learn
 numpy
 nltk
+pdfplumber
+python-multipart