feat: resume analysis

Author: Stanford997

code/app.py

@@ -1,9 +1,9 @@
 import json
 import os
 from flask import Flask, request, jsonify
-from pymongo import MongoClient
-from bson.objectid import ObjectId
+from pymongo.mongo_client import MongoClient
 from modules.upload import upload_parse_resume
+from modules.evaluator import evaluate_resume
 
 app = Flask(__name__)
 
@@ -13,20 +13,43 @@
 
 # MongoDB
 client = MongoClient(config['MONGO_URI'])
-
-db = client['resume_db']
-resume_collection = db['resumes']
+try:
+    database = client.get_database("resume_db")
+    resume_collection = database.get_collection("resumes")
+    query = {"user_id": "333"}
+    resume = resume_collection.find_one(query)
+    print(resume)
+except Exception as e:
+    raise Exception("Unable to find the document due to the following error: ", e)
 
 
 @app.route('/upload', methods=['POST'])
 def upload_resume():
     user_id = request.form.get('user_id')
     if not user_id:
         return jsonify({"error": "No user ID provided."}), 400
-    # vector the resume text
+    # Vector the resume text
     return upload_parse_resume(request, resume_collection)
 
-# def evaluation():
+
+@app.route('/resume_evaluate', methods=['POST'])
+def resume_evaluate():
+    user_id = request.form.get('user_id')
+    if not user_id:
+        return jsonify({"error": "No user ID provided."}), 400
+
+    # Load resume from database
+    resume = resume_collection.find_one({"user_id": user_id})
+    if not resume:
+        return jsonify({"error": "No resume found for this user."}), 404
+
+    resume_text = resume.get('resume_text', '')
+    if not resume_text:
+        return jsonify({"error": "Resume text is empty."}), 400
+
+    analysis_result = evaluate_resume(resume_text)
+
+    return jsonify({"analysis": analysis_result}), 200
 
 
 if __name__ == '__main__':

code/modules/evaluator.py

@@ -0,0 +1,111 @@
+"""
+This module is responsible for evaluating resumes based on specified criteria.
+"""
+
+import json
+import os
+from openai import OpenAI
+
+# Load the OpenAI API key
+config_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'configs', 'config.json')
+with open(config_path, 'r') as file:
+    config = json.load(file)
+client = OpenAI(
+    api_key=config['CHATGPT_API_KEY']
+)
+
+# Define the scoring criteria and weights
+SCORING_CRITERIA = {
+    'Relevance of Job Description': 30,
+    'Achievements and Impact': 25,
+    'Education and Certifications': 15,
+    'Resume Structure and Presentation': 10,
+    'Soft Skills': 10,
+    'Consistency and Chronology': 10
+}
+
+
+def evaluate_resume(resume_text):
+    """
+    Evaluate resumes by calling ChatGPT API
+    """
+    try:
+        prompt = f"""
+        Please evaluate the following resume based on the criteria below, and structure your response according to the format provided:
+
+        Criteria:
+        1. Relevance of Job Description (30%)
+        2. Achievements and Impact (25%)
+        3. Education and Certifications (15%)
+        4. Resume Structure and Presentation (10%)
+        5. Soft Skills (10%)
+        6. Consistency and Chronology (10%)
+
+        The response should follow this format:
+
+        ### Scores for each criteria:
+        1. Relevance of Job Description: X/100
+        2. Achievements and Impact: X/100
+        3. Education and Certifications: X/100
+        4. Resume Structure and Presentation: X/100
+        5. Soft Skills: X/100
+        6. Consistency and Chronology: X/100
+
+        ### Weighted Total Score: Y/100
+
+        ### Explanation for each criteria:
+        1. Relevance of Job Description:
+        [Explanation of the score]
+        2. Achievements and Impact:
+        [Explanation of the score]
+        3. Education and Certifications:
+        [Explanation of the score]
+        4. Resume Structure and Presentation:
+        [Explanation of the score]
+        5. Soft Skills:
+        [Explanation of the score]
+        6. Consistency and Chronology:
+        [Explanation of the score]
+
+        Resume: {resume_text}
+        """
+
+        # Call the evaluation API
+        response = client.chat.completions.create(
+            model='gpt-4o-mini',
+            messages=[{'role': 'system', 'content': 'You are a professional resume evaluator.'},
+                      {'role': 'user', 'content': prompt}],
+            max_tokens=500,
+            temperature=0.7
+        )
+
+        evaluation = response.choices[0].message.content
+        return evaluation
+
+    except Exception as e:
+        return f'Error evaluating resume: {str(e)}'
+
+
+def extract_scores_and_explanation(evaluation):
+    """
+    Extracts the scores and explanations from the evaluation response.
+    """
+    scores = {}
+    explanation = ''
+    lines = evaluation.split('\n')
+
+    # 6 criteria scores
+    scores['Relevance of Job Description'] = int(lines[1].split(':')[1].strip().replace('/100', ''))
+    scores['Achievements and Impact'] = int(lines[2].split(':')[1].strip().replace('/100', ''))
+    scores['Education and Certifications'] = int(lines[3].split(':')[1].strip().replace('/100', ''))
+    scores['Resume Structure and Presentation'] = int(lines[4].split(':')[1].strip().replace('/100', ''))
+    scores['Soft Skills'] = int(lines[5].split(':')[1].strip().replace('/100', ''))
+    scores['Consistency and Chronology'] = int(lines[6].split(':')[1].strip().replace('/100', ''))
+
+    # Weighted total score
+    weighted_score = float(lines[8].split(':')[1].strip().replace('/100', ''))
+
+    # Explanation part
+    explanation = '\n'.join(lines[11:])
+
+    return scores, weighted_score, explanation

code/preporcess/gpt_evalution.py renamed to code/preprocess/gpt_evalution.py

@@ -4,12 +4,17 @@
 and then outputs the scores and explanations to a new CSV file.
 """
 
+import json
+import os
 import pandas as pd
 from openai import OpenAI
 
-# OpenAI Key
+# Load the OpenAI API key
+config_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'configs', 'config.json')
+with open(config_path, 'r') as file:
+    config = json.load(file)
 client = OpenAI(
-    api_key='YOUR-GPT-API-KEY'
+    api_key=config['CHATGPT_API_KEY']
 )
 
 # Define the scoring criteria and weights