added retry logic to config query, fixed api variables, fixed score and summary functions

.gitignore

@@ -15,8 +15,12 @@ venv/
 # Ignore all files in a specific folder
 build/
 
+
 # Do not ignore a specific file inside an ignored directory
 !build/keep-me.txt
 
 # ignore cache files
-__pycache__/
+__pycache_/
+.pytest_cache/
+# Ignore all files with the .tmp extension
+*.tmp

config.py

@@ -2,6 +2,7 @@ import os
 from dotenv import load_dotenv
 from supabase import create_client
 import requests
+import time
 
 # Load environment variables from .env file
 load_dotenv()
@@ -13,74 +14,54 @@ if not SUPABASE_KEY:
     raise ValueError("SUPABASE_KEY is not set in the environment variables.")
 supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
 
+# Hugging Face API Config
 HF_MODELS = {
     "gemma": "https://api-inference.huggingface.co/models/google/gemma-7b",
     "bart": "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
 }
 
-# Hugging Face API Config
-#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"}
-
-# Ensure the API key is loaded
 if not HF_API_TOKEN:
     raise ValueError("Missing Hugging Face API key. Check your .env file.")
 
-# 
-def query(payload, model="gemma"):
+# Correct API Headers
+HF_HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+
+def query(payload, model="gemma", retries=3, delay=5):
     """
-    Sends a request to the selected Hugging Face model API.
+    Sends a request to the Hugging Face API with retries.
 
     :param payload: The input data for inference.
-    :param model: Choose either 'gemma' (for google/gemma-7b) or 'bart' (for facebook/bart-large-cnn).
-    :return: The model's response in JSON format, or None if the request fails.
+    :param model: The model name ('gemma' or 'bart').
+    :param retries: Number of times to retry if the request fails.
+    :param delay: Delay in seconds before retrying.
+    :return: The model's response in JSON format, or None if all retries fail.
     """
     if model not in HF_MODELS:
         raise ValueError("Invalid model name. Choose 'gemma' or 'bart'.")
 
-    api_url = f"https://api-inference.huggingface.co/models/{HF_MODELS[model]}"
-
-    try:
-        response = requests.post(api_url, headers=HF_HEADERS, json=payload)
-
-        if response.status_code == 401:
-            print(f"Error querying Hugging Face model '{model}': 401 Unauthorized. Check API key.")
-            return None  # Handle authentication failure
-
-        response.raise_for_status()  # Raise an error for failed requests (e.g., 500 errors)
-
-        return response.json()  # Return the parsed JSON response
+    api_url = HF_MODELS[model]  # Correct model URL
 
-    except requests.exceptions.RequestException as e:
-        print(f"Error querying Hugging Face model '{model}': {e}")
-        return None  # Return None if API call fails
-
-# Bart query
-def query(payload, model="bart"):
-    """
-    Sends a request to the selected Hugging Face model API.
-
-    :param payload: The input data for inference.
-    :param model: Choose either 'gemma' (for google/gemma-7b) or 'bart' (for facebook/bart-large-cnn).
-    :return: The model's response in JSON format, or None if the request fails.
-    """
-    if model not in HF_MODELS:
-        raise ValueError("Invalid model name. Choose 'gemma' or 'bart'.")
+    for attempt in range(retries):
+        try:
+            response = requests.post(api_url, headers=HF_HEADERS, json=payload)
 
-    api_url = f"https://api-inference.huggingface.co/models/{HF_MODELS[model]}"
+            if response.status_code == 401:
+                print(f"Error querying Hugging Face model '{model}': 401 Unauthorized. Check API key.")
+                return None  # API key issue
 
-    try:
-        response = requests.post(api_url, headers=HF_HEADERS, json=payload)
+            if response.status_code == 500:
+                print(f"Server error (500) on attempt {attempt + 1}. Retrying in {delay} seconds...")
+                time.sleep(delay)  # Wait before retrying
+                continue  # Retry the request
 
-        if response.status_code == 401:
-            print(f"Error querying Hugging Face model '{model}': 401 Unauthorized. Check API key.")
-            return None  # Handle authentication failure
+            response.raise_for_status()  # Raise an error for failed requests (except 500)
 
-        response.raise_for_status()  # Raise an error for failed requests (e.g., 500 errors)
+            return response.json()  # Return the parsed JSON response
 
-        return response.json()  # Return the parsed JSON response
+        except requests.exceptions.RequestException as e:
+            print(f"Error querying Hugging Face model '{model}': {e}")
+            time.sleep(delay)  # Wait before retrying
 
-    except requests.exceptions.RequestException as e:
-        print(f"Error querying Hugging Face model '{model}': {e}")
-        return None  # Return None if API call fails
+    print("All retry attempts failed.")
+    return None  # Return None if all retries fail

utils.py

@@ -43,26 +43,36 @@ def extract_email(resume_text):
     match = re.search(r"[\w\.-]+@[\w\.-]+", resume_text)
     return match.group(0) if match else None
 
-# Test on why score 0 is returned even though resume matches key words
-# score_candidate function will use HuggingFace gemini model
 def score_candidate(resume_text, job_description):
     """
     Scores the candidate's resume based on the job description using the Hugging Face API.
-
+    
     :param resume_text: The extracted resume text.
     :param job_description: The job description for comparison.
     :return: A numerical score (default 0 if scoring fails).
     """
     payload = {"inputs": f"Resume: {resume_text}\nJob Description: {job_description}"}
-    response_gemma = query(payload, model="gemma")  # Use Google Gemma Model for scoring
+    response_gemma = query(payload, model="gemma")  # Call Hugging Face API
 
     if response_gemma is None:
-        return 0  # Return 0 if API call fails
+        print("API response is None")
+        return 0  
+
+    print("API Response:", response_gemma)  # Debugging
+
+    # Handle list response
+    if isinstance(response_gemma, list) and len(response_gemma) > 0:
+        response_gemma = response_gemma[0]  # Extract first item if response is a list
 
     try:
-        return float(response_gemma.get("score", 0))  # Ensure score is always a float
-    except (TypeError, ValueError):
-        return 0  # Return 0 if score parsing fails
+        if isinstance(response_gemma, dict) and "score" in response_gemma:
+            return float(response_gemma["score"])
+        else:
+            print("Unexpected API response format:", response_gemma)  # Debugging
+            return 0  # Default if score is missing
+    except (TypeError, ValueError) as e:
+        print(f"Error parsing score: {e}")
+        return 0  
 
 # summarize_resume function will use HuggingFace BART model
 def summarize_resume(resume_text):