Update app.py

app.py

@@ -1,322 +1,64 @@
-import gradio as gr
 import pandas as pd
 import numpy as np
-from course_recommender import CourseRecommender
 from database_connection import DatabaseConnection
-import os
 
-# Initialize the recommender system
-recommender = CourseRecommender()
-db_connection = DatabaseConnection()
-
-# Global variables to store current recommendations
-current_recommendations = []
-current_user_input = {}
-
-def get_course_recommendations(stanine, gwa, strand, hobbies):
-    """Get course recommendations based on user input"""
-    global current_recommendations, current_user_input
+def create_basic_training_data():
+    """Create basic training data for the course recommender - DEPRECATED"""
+    print("WARNING: Basic training data is deprecated. Use student feedback data instead.")
+    raise ValueError("Basic training data is no longer used. Please use student feedback data from /student_feedback_counts endpoint.")
     
-    # Validate inputs
-    if not stanine or not gwa or not strand or not hobbies:
-        return "Please fill in all fields", "", "", "", "", ""
+    # Define strands
+    strands = ["STEM", "ABM", "HUMSS", "GAS", "TVL"]
     
-    try:
-        stanine = int(stanine)
-        gwa = float(gwa)
-        
-        if stanine < 1 or stanine > 9:
-            return "Stanine must be between 1-9", "", "", "", "", ""
-        
-        if gwa < 75 or gwa > 100:
-            return "GWA must be between 75-100", "", "", "", "", ""
-        
-        # Normalize strand to uppercase for case-insensitive matching
-        strand = strand.upper()
+    # Define common hobbies
+    hobbies_list = [
+        "Programming", "Reading", "Sports", "Music", "Art", "Gaming",
+        "Photography", "Writing", "Dancing", "Cooking", "Traveling",
+        "Mathematics", "Science", "History", "Literature", "Technology"
+    ]
+    
+    # Generate synthetic data
+    np.random.seed(42)  # For reproducible results
+    n_samples = 1000
+    
+    data = []
+    for _ in range(n_samples):
+        # Generate random but realistic data
+        stanine = np.random.randint(1, 10)
+        gwa = np.random.uniform(75, 100)  # GWA between 75-100
+        strand = np.random.choice(strands)
+        course = np.random.choice(courses)
+        hobbies = np.random.choice(hobbies_list, size=np.random.randint(1, 4), replace=False)
+        hobbies_str = ", ".join(hobbies)
+        
+        # Generate rating based on some logic
+        if stanine >= 7 and gwa >= 85:
+            rating = np.random.choice([4, 5], p=[0.3, 0.7])
+        elif stanine >= 5 and gwa >= 80:
+            rating = np.random.choice([3, 4, 5], p=[0.2, 0.5, 0.3])
+        else:
+            rating = np.random.choice([1, 2, 3, 4], p=[0.1, 0.3, 0.4, 0.2])
         
-        if strand not in ["STEM", "ABM", "HUMSS", "GAS", "TVL"]:
-            return "Strand must be one of: STEM, ABM, HUMSS, GAS, TVL", "", "", "", "", ""
+        count = np.random.randint(1, 10)
         
-        # Store current user input
-        current_user_input = {
+        data.append({
+            'course': course,
             'stanine': stanine,
             'gwa': gwa,
             'strand': strand,
-            'hobbies': hobbies
-        }
-        
-        # Get recommendations
-        recommendations = recommender.predict_course(stanine, gwa, strand, hobbies)
-        current_recommendations = recommendations
-        
-        # Format top 3 recommendations
-        if len(recommendations) >= 3:
-            course1 = f"{recommendations[0][0]} (Confidence: {recommendations[0][1]*100:.1f}%)"
-            course2 = f"{recommendations[1][0]} (Confidence: {recommendations[1][1]*100:.1f}%)"
-            course3 = f"{recommendations[2][0]} (Confidence: {recommendations[2][1]*100:.1f}%)"
-        elif len(recommendations) == 2:
-            course1 = f"{recommendations[0][0]} (Confidence: {recommendations[0][1]*100:.1f}%)"
-            course2 = f"{recommendations[1][0]} (Confidence: {recommendations[1][1]*100:.1f}%)"
-            course3 = "No third recommendation available"
-        elif len(recommendations) == 1:
-            course1 = f"{recommendations[0][0]} (Confidence: {recommendations[0][1]*100:.1f}%)"
-            course2 = "No second recommendation available"
-            course3 = "No third recommendation available"
-        else:
-            course1 = "No recommendations available"
-            course2 = ""
-            course3 = ""
-        
-        return course1, course2, course3, None, None, None
-        
-    except ValueError as e:
-        return f"Invalid input: {str(e)}", "", "", None, None, None
-    except Exception as e:
-        return f"Error getting recommendations: {str(e)}", "", "", None, None, None
-
-def submit_all_ratings(course1_rating, course2_rating, course3_rating):
-    """Submit ratings for all three recommendations"""
-    global current_recommendations, current_user_input
+            'rating': rating,
+            'hobbies': hobbies_str,
+            'count': count
+        })
     
-    if not current_recommendations or not current_user_input:
-        return "No recommendations to rate. Please get recommendations first."
-    
-    try:
-        results = []
-        ratings_submitted = 0
-        
-        # Rate first recommendation
-        if course1_rating and len(current_recommendations) >= 1:
-            rating_value = "like" if course1_rating == "👍 Like" else "dislike"
-            course = current_recommendations[0][0]
-            success = recommender.add_feedback_with_learning(
-                course=course,
-                stanine=current_user_input['stanine'],
-                gwa=current_user_input['gwa'],
-                strand=current_user_input['strand'],
-                rating=rating_value,
-                hobbies=current_user_input['hobbies']
-            )
-            if success:
-                results.append(f"✅ Rating for '{course}' recorded")
-                ratings_submitted += 1
-            else:
-                results.append(f"❌ Failed to record rating for '{course}'")
-        
-        # Rate second recommendation
-        if course2_rating and len(current_recommendations) >= 2:
-            rating_value = "like" if course2_rating == "👍 Like" else "dislike"
-            course = current_recommendations[1][0]
-            success = recommender.add_feedback_with_learning(
-                course=course,
-                stanine=current_user_input['stanine'],
-                gwa=current_user_input['gwa'],
-                strand=current_user_input['strand'],
-                rating=rating_value,
-                hobbies=current_user_input['hobbies']
-            )
-            if success:
-                results.append(f"✅ Rating for '{course}' recorded")
-                ratings_submitted += 1
-            else:
-                results.append(f"❌ Failed to record rating for '{course}'")
-        
-        # Rate third recommendation
-        if course3_rating and len(current_recommendations) >= 3:
-            rating_value = "like" if course3_rating == "👍 Like" else "dislike"
-            course = current_recommendations[2][0]
-            success = recommender.add_feedback_with_learning(
-                course=course,
-                stanine=current_user_input['stanine'],
-                gwa=current_user_input['gwa'],
-                strand=current_user_input['strand'],
-                rating=rating_value,
-                hobbies=current_user_input['hobbies']
-            )
-            if success:
-                results.append(f"✅ Rating for '{course}' recorded")
-                ratings_submitted += 1
-            else:
-                results.append(f"❌ Failed to record rating for '{course}'")
-        
-        if ratings_submitted > 0:
-            return f"Thank you! {ratings_submitted} rating(s) submitted successfully.\n\n" + "\n".join(results)
-        else:
-            return "Please select at least one rating before submitting."
-            
-    except Exception as e:
-        return f"Error recording feedback: {str(e)}"
+    return pd.DataFrame(data)
 
-def train_model():
-    """Train the model with current data"""
-    try:
-        accuracy = recommender.train_model(use_database=True)
-        return f"✅ Model trained successfully! Accuracy: {accuracy:.3f}"
-    except Exception as e:
-        return f"❌ Error training model: {str(e)}"
+def save_basic_data():
+    """Save basic training data to CSV"""
+    df = create_basic_training_data()
+    df.to_csv('basic_training_data.csv', index=False)
+    print(f"Basic training data saved with {len(df)} samples")
+    return df
 
-def get_available_courses_info():
-    """Get information about available courses from database"""
-    try:
-        courses = db_connection.get_available_courses()
-        if courses:
-            return f"📚 Available courses in database: {len(courses)}\n\n" + "\n".join([f"• {course}" for course in courses[:10]]) + (f"\n... and {len(courses)-10} more" if len(courses) > 10 else "")
-        else:
-            return "📚 No courses found in database. Please check the /courses endpoint."
-    except Exception as e:
-        return f"❌ Error fetching courses: {str(e)}"
-
-# Create Gradio interface
-def create_interface():
-    with gr.Blocks(title="Course AI Recommender", theme=gr.themes.Soft()) as demo:
-        gr.Markdown("""
-        # 🎓 Course AI Machine Learning Recommender
-        
-        Get personalized course recommendations based on your academic profile and interests!
-        """)
-        
-        with gr.Row():
-            with gr.Column(scale=1):
-                gr.Markdown("### 📝 Your Profile")
-                
-                stanine_input = gr.Textbox(
-                    label="Stanine Score (1-9)",
-                    placeholder="Enter your stanine score (1-9)",
-                    info="Your stanine score from standardized tests"
-                )
-                
-                gwa_input = gr.Textbox(
-                    label="GWA (75-100)",
-                    placeholder="Enter your GWA (75-100)",
-                    info="Your Grade Weighted Average"
-                )
-                
-                strand_input = gr.Dropdown(
-                    choices=["STEM", "ABM", "HUMSS", "GAS", "TVL"],
-                    value="STEM",
-                    label="Academic Strand",
-                    info="Your current academic strand"
-                )
-                
-                hobbies_input = gr.Textbox(
-                    label="Hobbies & Interests",
-                    placeholder="e.g., Programming, Reading, Sports, Music",
-                    info="List your hobbies and interests (comma-separated)"
-                )
-                
-                get_recommendations_btn = gr.Button("🎯 Get Recommendations", variant="primary")
-                
-                train_model_btn = gr.Button("🤖 Train Model", variant="secondary")
-                
-                show_courses_btn = gr.Button("📚 Show Available Courses", variant="secondary")
-            
-            with gr.Column(scale=1):
-                gr.Markdown("### 🎓 Top 3 Course Recommendations")
-                
-                # Display top 3 recommendations
-                course1_output = gr.Textbox(
-                    label="1st Recommendation",
-                    interactive=False
-                )
-                
-                course1_rating = gr.Radio(
-                    choices=["👍 Like", "👎 Dislike"],
-                    label="Rate 1st Recommendation",
-                    interactive=True
-                )
-                
-                course2_output = gr.Textbox(
-                    label="2nd Recommendation", 
-                    interactive=False
-                )
-                
-                course2_rating = gr.Radio(
-                    choices=["👍 Like", "👎 Dislike"],
-                    label="Rate 2nd Recommendation",
-                    interactive=True
-                )
-                
-                course3_output = gr.Textbox(
-                    label="3rd Recommendation",
-                    interactive=False
-                )
-                
-                course3_rating = gr.Radio(
-                    choices=["👍 Like", "👎 Dislike"],
-                    label="Rate 3rd Recommendation",
-                    interactive=True
-                )
-                
-                submit_ratings_btn = gr.Button("Submit All Ratings", variant="primary")
-                
-                rating_feedback = gr.Textbox(
-                    label="Rating Feedback",
-                    interactive=False
-                )
-                
-                courses_info = gr.Textbox(
-                    label="Available Courses",
-                    lines=8,
-                    interactive=False
-                )
-        
-        # Event handlers
-        get_recommendations_btn.click(
-            fn=get_course_recommendations,
-            inputs=[stanine_input, gwa_input, strand_input, hobbies_input],
-            outputs=[course1_output, course2_output, course3_output, course1_rating, course2_rating, course3_rating]
-        )
-        
-        submit_ratings_btn.click(
-            fn=submit_all_ratings,
-            inputs=[course1_rating, course2_rating, course3_rating],
-            outputs=[rating_feedback]
-        )
-        
-        train_model_btn.click(
-            fn=train_model,
-            outputs=[gr.Textbox(label="Training Status", interactive=False)]
-        )
-        
-        show_courses_btn.click(
-            fn=get_available_courses_info,
-            outputs=[courses_info]
-        )
-        
-        # Add some example inputs
-        gr.Markdown("""
-        ### 💡 Example Inputs
-        
-        **For STEM students:**
-        - Stanine: 7-9, GWA: 85-95, Strand: STEM, Hobbies: Programming, Mathematics, Science
-        
-        **For ABM students:**
-        - Stanine: 6-8, GWA: 80-90, Strand: ABM, Hobbies: Business, Leadership, Economics
-        
-        **For HUMSS students:**
-        - Stanine: 5-8, GWA: 78-88, Strand: HUMSS, Hobbies: Literature, History, Writing
-        """)
-    
-    return demo
-
-# Initialize the interface
 if __name__ == "__main__":
-    # Try to load existing model
-    try:
-        recommender.load_model()
-        print("✅ Loaded existing model")
-    except:
-        print("⚠️ No existing model found. Training with basic data...")
-        try:
-            recommender.train_model(use_database=False)
-            print("✅ Model trained with basic data")
-        except Exception as e:
-            print(f"❌ Error training model: {e}")
-    
-    # Create and launch interface
-    demo = create_interface()
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=True
-    )
+    save_basic_data()