Update app.py

app.py

@@ -1,46 +1,80 @@
 from flask import Flask, request, jsonify
-from flask_cors import CORS
-import joblib
-import pandas as pd
-import os
+import math
 
 app = Flask(__name__)
-CORS(app)
 
-# In Spaces, files are in the same directory
-model = joblib.load('alumni_match_model.joblib')
-model_columns = joblib.load('model_feature_columns.joblib')
+# --- New, Smarter Scoring Logic for Alumni Matching ---
+# We define weights for skills. More technical/in-demand skills get a higher weight.
+skill_weights = {
+    'java': 3, 'python': 3, 'react': 3, 'c++': 3, 'sql': 3, 'aws': 3, 'ai': 3, 'ml': 3,
+    'tensorflow': 3, 'nodejs': 3, 'swift': 3, 'ios': 3, 'cybersecurity': 3,
+    'devops': 3, 'docker': 3, 'go': 3, 'microservices': 3, 'qa': 3, 'selenium': 3,
+    'data science': 3, 'vhdl': 3, 'verilog': 3, 'embedded c': 3, 'rtos': 3,
+    'vlsi': 3, 'cad': 3, 'solidworks': 3, 'robotics': 3, 'staadpro': 3,
+    'html': 2, 'css': 2, 'javascript': 2, 'ui/ux': 2, 'figma': 2, 'autocad': 2,
+    'thermodynamics': 2, 'manufacturing': 2, 'signal processing': 2, 'analog design': 2,
+}
 
-@app.route('/', methods=['POST'])
-def handler():
-    incoming_data = request.get_json()
-    df = pd.DataFrame([incoming_data])
+# A significant bonus if the viewer's branch matches the target alumni's branch
+BRANCH_MATCH_BONUS = 10
+
+def calculate_alumni_score(viewer_profile, target_profile):
+    # The frontend sends skills as a string like "python|react|sql". We need to split it.
+    viewer_skills = set(str(viewer_profile.get('viewer_skills', '')).lower().split('|'))
+    target_skills = set(str(target_profile.get('target_skills', '')).lower().split('|'))
+    viewer_branch = str(viewer_profile.get('viewer_branch', '')).lower().strip()
+    target_branch = str(target_profile.get('target_branch', '')).lower().strip()
 
-    def count_common_skills(row):
-        viewer_skills = set(str(row.get('viewer_skills', '')).lower().split('|'))
-        target_skills = set(str(row.get('target_skills', '')).lower().split('|'))
-        return len(viewer_skills.intersection(target_skills))
+    # 1. Calculate a score based on shared skills, using our weights
+    common_skills = viewer_skills.intersection(target_skills)
+    skill_score = sum(skill_weights.get(skill, 1) for skill in common_skills)
 
-    df['common_skills_count'] = df.apply(count_common_skills, axis=1)
-    df['branch_match'] = (df['viewer_branch'].str.lower() == df['target_branch'].str.lower()).astype(int)
+    # 2. Add the bonus if branches match
+    branch_score = 0
+    if viewer_branch and target_branch and viewer_branch == target_branch:
+        branch_score = BRANCH_MATCH_BONUS
 
-    for col in model_columns:
-        if col.startswith('company_'):
-            df[col] = 0
+    # 3. Combine the scores
+    raw_score = skill_score + branch_score
+    
+    # 4. Normalize the raw score to a clean 1-10 scale for the UI
+    # This uses a logarithmic scale to make the scores feel more natural.
+    # A small match gets a score of 1-3, a good match 4-7, a great match 8-10.
+    if raw_score == 0:
+        return 0
     
-    company_name = incoming_data.get('target_company', '')
-    if company_name:
-        company_col_name = f"company_{company_name}"
-        if company_col_name in df.columns:
-            df[company_col_name] = 1
-
-    final_df = df[model_columns]
-    prediction_proba = model.predict_proba(final_df)
-    match_probability = prediction_proba[0][1]
-    final_score = round(match_probability * 10)
-
-    return jsonify({'score': final_score})
-
-# ADD THESE FINAL TWO LINES TO START THE SERVER
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+    normalized_score = min(10, math.log(raw_score + 1) * 2.5)
+    
+    # We return a score rounded to one decimal place, like 7.5
+    return round(normalized_score, 1)
+
+
+@app.route('/', methods=['POST'])
+def get_match_score():
+    try:
+        data = request.get_json()
+        
+        # The frontend sends data with these keys, so we match them here
+        viewer_profile = {
+            'viewer_skills': data.get('viewer_skills'),
+            'viewer_branch': data.get('viewer_branch')
+        }
+        target_profile = {
+            'target_skills': data.get('target_skills'),
+            'target_branch': data.get('target_branch')
+        }
+
+        if not viewer_profile or not target_profile:
+            return jsonify({"error": "Missing profile data"}), 400
+            
+        final_score = calculate_alumni_score(viewer_profile, target_profile)
+
+        return jsonify({"score": final_score})
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return jsonify({"error": "An internal error occurred."}), 500
+
+if __name__ == '__main__':
+    # This allows you to run the app locally for testing
+    app.run(debug=True)