mvp app tested, configured postgreSQL Supabase

.gitignore

@@ -16,4 +16,7 @@ venv/
 build/
 
 # Do not ignore a specific file inside an ignored directory
-!build/keep-me.txt
+!build/keep-me.txt
+
+# ignore cache files
+__pycache__/

config.py

@@ -13,6 +13,17 @@ if not SUPABASE_KEY:
 supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
 
 # Hugging Face API Config
-HF_API_URL = "https://api-inference.huggingface.co/models/google/gemma-7b"
+HF_API_URL = "https://router.huggingface.co/hf-inference/models/google/gemma-7b"
 HF_API_TOKEN = os.getenv("HF_API_TOKEN")
-HF_HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+HF_HEADERS = {"Authorization": f"Bearer HF_API_TOKEN"}
+
+def query(payload):
+    """Sends request to Hugging Face inference API."""
+    import requests
+    response = requests.post(HF_API_URL, headers=HF_HEADERS, json=payload)
+    
+    if response.status_code != 200:
+        print(f"Error: {response.status_code}, {response.text}")  # Debugging
+        return None
+
+    return response.json()

main.py

@@ -1,5 +1,12 @@
 import streamlit as st
-from utils import process_resumes, generate_pdf_report
+from utils import process_resumes, generate_pdf_report, store_in_supabase, score_candidate, extract_email, parse_resume
+from config import supabase
+from config import HF_API_TOKEN, HF_API_URL, HF_HEADERS
+import fitz  # PyMuPDF
+from io import BytesIO
+from dotenv import load_dotenv
+import os
+import requests
 
 def main():
     st.title("AI Candidate Screening App")

utils.py

@@ -4,7 +4,10 @@ import json
 import re
 from io import BytesIO
 import supabase
-from config import SUPABASE_URL, SUPABASE_KEY, HF_API_TOKEN, HF_API_URL, HF_HEADERS
+from config import SUPABASE_URL, SUPABASE_KEY, HF_API_TOKEN, HF_API_URL, HF_HEADERS, supabase
+#from config import supabase
+
+# These functions will be called in the main.py file
 
 def parse_resume(pdf_file):
     """Extracts text from a resume PDF."""
@@ -20,8 +23,8 @@ def extract_email(resume_text):
 def score_candidate(resume_text, job_description):
     """Sends resume and job description to Hugging Face for scoring."""
     payload = {"inputs": f"Resume: {resume_text}\nJob Description: {job_description}"}
-    response = requests.post(HF_API_URL, headers=HF_HEADERS, data=json.dumps(payload))
-    return response.json().get("score", 0)
+    response = requests.post(HF_API_URL, headers=HF_HEADERS, json=payload)
+
     # Debugging: Print response
     if response.status_code != 200:
         print(f"Error: {response.status_code}, {response.text}")  # Log any errors
@@ -33,16 +36,18 @@ def score_candidate(resume_text, job_description):
         print("Failed to decode JSON response:", response.text)  # Debugging output
         return 0  # Return default score if JSON decoding fails
 
-def store_in_supabase(resume_text, score, candidate_name):
-    """Stores resume data in Supabase."""
-    email = extract_email(resume_text)
+def store_in_supabase(resume_text, score, candidate_name, email, summary):
+    """Stores candidate data in Supabase"""
     data = {
-        "name": candidate_name, 
-        "resume": resume_text, 
-        "score": score, 
-        "email": email
+        "name": candidate_name,
+        "resume": resume_text,
+        "score": score,
+        "email": email,
+        "summary": summary  # Store summary in Supabase
     }
-    supabase.table("candidates").insert(data).execute()
+
+    response = supabase.table("candidates").insert(data).execute()
+    print("Inserted into Supabase:", response)  # Debugging output
 
 def generate_pdf_report(shortlisted_candidates):
     """Generates a PDF summary of shortlisted candidates."""
@@ -50,7 +55,11 @@ def generate_pdf_report(shortlisted_candidates):
     doc = fitz.open()
     for candidate in shortlisted_candidates:
         page = doc.new_page()
-        page.insert_text((50, 50), f"Candidate: {candidate['name']}\nEmail: {candidate['email']}\nScore: {candidate['score']}\nSummary: {candidate['summary']}")
+        summary = candidate.get("summary", "No summary available")  # Avoid KeyError
+        page.insert_text(
+            (50, 50),
+            f"Candidate: {candidate['name']}\nEmail: {candidate['email']}\nScore: {candidate['score']}\nSummary: {summary}"
+        )
     doc.save(pdf)
     pdf.seek(0)
     return pdf
@@ -62,11 +71,19 @@ def process_resumes(uploaded_files, job_description):
         resume_text = parse_resume(pdf_file)
         score = score_candidate(resume_text, job_description)
         email = extract_email(resume_text)
+
+        # Generate summary (replace with actual summarization logic later)
+        summary = f"{pdf_file.name} has a score of {score} for this job."
+
         candidates.append({
-            "name": pdf_file.name, 
-            "resume": resume_text, 
-            "score": score, 
-            "email": email
+            "name": pdf_file.name,
+            "resume": resume_text,
+            "score": score,
+            "email": email,
+            "summary": summary
         })
-        store_in_supabase(resume_text, score, pdf_file.name, email)
-    return sorted(candidates, key=lambda x: x["score"], reverse=True)[:5]
+
+        # Store all details including summary in Supabase
+        store_in_supabase(resume_text, score, pdf_file.name, email, summary)
+
+    return sorted(candidates, key=lambda x: x["score"], reverse=True)[:5]  # Return top 5 candidates