Update app.py

app.py

@@ -1,64 +1,210 @@
-# app.py
-import gradio as gr
-
-def chatbot_response(message, history):
-    """Process user input and return chatbot response"""
-    user_input = message.lower()
-    
-    if "hello" in user_input:
-        return "Hello there! How can I help you today?"
-    elif "bye" in user_input:
-        return "Goodbye! 👋"
-    else:
-        return f"You said: {message}. I'm still learning!"
-
-# Create Gradio interface
-with gr.Blocks(title="AI Chatbot", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🤖 AI Chatbot")
-    gr.Markdown("Welcome to my simple AI chatbot! Try saying 'hello' or 'bye'.")
-    
-    # Chat interface
-    chatbot = gr.Chatbot(
-        label="Chat",
-        height=400,
-        show_label=True,
-        container=True,
-        bubble_full_width=False
-    )
-    
-    # Text input
-    msg = gr.Textbox(
-        label="Your message",
-        placeholder="Type your message here...",
-        lines=1,
-        max_lines=3,
-        show_label=True
-    )
-    
-    # Submit button
-    submit_btn = gr.Button("Send", variant="primary")
-    
-    # Clear button
-    clear_btn = gr.Button("Clear", variant="secondary")
-    
-    # Event handlers
-    def user(user_message, history):
-        return "", history + [[user_message, None]]
-    
-    def bot(history):
-        user_message = history[-1][0]
-        bot_message = chatbot_response(user_message, history)
-        history[-1][1] = bot_message
-        return history
-    
-    # Connect the interface
-    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot, chatbot, chatbot
-    )
-    submit_btn.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot, chatbot, chatbot
-    )
-    clear_btn.click(lambda: None, None, chatbot, queue=False)
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+import os
+import gradio as gr
+import pandas as pd
+import numpy as np
+from ai_chatbot import AIChatbot
+from database_recommender import CourseRecommender
+import warnings
+import logging
+
+# Suppress warnings
+warnings.filterwarnings('ignore')
+logging.getLogger('tensorflow').setLevel(logging.ERROR)
+
+# Initialize components
+try:
+    chatbot = AIChatbot()
+    print("✅ Chatbot initialized successfully")
+except Exception as e:
+    print(f"⚠️  Warning: Could not initialize chatbot: {e}")
+    chatbot = None
+
+try:
+    recommender = CourseRecommender()
+    print("✅ Recommender initialized successfully")
+except Exception as e:
+    print(f"⚠️  Warning: Could not initialize recommender: {e}")
+    recommender = None
+
+def chat_with_bot(message, history):
+    """Handle chatbot interactions"""
+    if chatbot is None:
+        return "Sorry, the chatbot is not available at the moment. Please try again later."
+    
+    if not message.strip():
+        return "Please enter a question."
+    
+    # Get answer from chatbot
+    answer, confidence = chatbot.find_best_match(message)
+    
+    # Get suggested questions
+    suggested_questions = chatbot.get_suggested_questions(message)
+    
+    # Format response
+    response = f"**Answer:** {answer}\n\n"
+    response += f"**Confidence:** {confidence:.2f}\n\n"
+    
+    if suggested_questions:
+        response += "**Suggested Questions:**\n"
+        for i, q in enumerate(suggested_questions, 1):
+            response += f"{i}. {q}\n"
+    
+    return response
+
+def get_course_recommendations(stanine, gwa, strand, hobbies):
+    """Get course recommendations"""
+    if recommender is None:
+        return "Sorry, the recommendation system is not available at the moment. Please try again later."
+    
+    try:
+        # Validate inputs
+        stanine = int(stanine)
+        gwa = float(gwa)
+        
+        if not (1 <= stanine <= 9):
+            return "❌ Stanine score must be between 1 and 9"
+        
+        if not (75 <= gwa <= 100):
+            return "❌ GWA must be between 75 and 100"
+        
+        if not strand:
+            return "❌ Please select a strand"
+        
+        if not hobbies.strip():
+            return "❌ Please enter your hobbies/interests"
+        
+        # Get recommendations
+        recommendations = recommender.recommend_courses(
+            stanine=stanine,
+            gwa=gwa,
+            strand=strand,
+            hobbies=hobbies
+        )
+        
+        if not recommendations:
+            return "No recommendations available at the moment."
+        
+        # Format recommendations
+        response = f"## 🎯 Course Recommendations for You\n\n"
+        response += f"**Profile:** Stanine {stanine}, GWA {gwa}, {strand} Strand\n"
+        response += f"**Interests:** {hobbies}\n\n"
+        
+        for i, rec in enumerate(recommendations, 1):
+            response += f"### {i}. {rec['code']} - {rec['name']}\n"
+            response += f"**Match Score:** {rec.get('rating', rec.get('probability', 0)):.1f}%\n\n"
+        
+        return response
+        
+    except Exception as e:
+        return f"❌ Error getting recommendations: {str(e)}"
+
+def get_faqs():
+    """Get available FAQs"""
+    if chatbot and chatbot.faqs:
+        faq_text = "## 📚 Frequently Asked Questions\n\n"
+        for i, faq in enumerate(chatbot.faqs, 1):
+            faq_text += f"**{i}. {faq['question']}**\n"
+            faq_text += f"{faq['answer']}\n\n"
+        return faq_text
+    return "No FAQs available at the moment."
+
+def get_available_courses():
+    """Get available courses"""
+    if recommender and recommender.courses:
+        course_text = "## 🎓 Available Courses\n\n"
+        for code, name in recommender.courses.items():
+            course_text += f"**{code}** - {name}\n"
+        return course_text
+    return "No courses available at the moment."
+
+# Create Gradio interface
+with gr.Blocks(title="PSAU AI Chatbot & Course Recommender", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🤖 PSAU AI Chatbot & Course Recommender
+        
+        Welcome to the Pangasinan State University AI-powered admission assistant! 
+        Get instant answers to your questions and receive personalized course recommendations.
+        """
+    )
+    
+    with gr.Tabs():
+        # Chatbot Tab
+        with gr.Tab("🤖 AI Chatbot"):
+            gr.Markdown("Ask me anything about university admissions, requirements, or general information!")
+            
+            chatbot_interface = gr.ChatInterface(
+                fn=chat_with_bot,
+                title="PSAU Admission Assistant",
+                description="Type your question below and get instant answers!",
+                examples=[
+                    "What are the admission requirements?",
+                    "When is the application deadline?",
+                    "How much is the tuition fee?",
+                    "Do you offer scholarships?",
+                    "What courses are available?"
+                ],
+                cache_examples=True
+            )
+        
+        # Course Recommender Tab
+        with gr.Tab("🎯 Course Recommender"):
+            gr.Markdown("Get personalized course recommendations based on your academic profile and interests!")
+            
+            with gr.Row():
+                with gr.Column():
+                    stanine_input = gr.Slider(
+                        minimum=1, maximum=9, step=1, value=7,
+                        label="Stanine Score (1-9)",
+                        info="Your stanine score from entrance examination"
+                    )
+                    gwa_input = gr.Slider(
+                        minimum=75, maximum=100, step=0.1, value=85.0,
+                        label="GWA (75-100)",
+                        info="Your General Weighted Average"
+                    )
+                    strand_input = gr.Dropdown(
+                        choices=["STEM", "ABM", "HUMSS"],
+                        value="STEM",
+                        label="High School Strand",
+                        info="Your senior high school strand"
+                    )
+                    hobbies_input = gr.Textbox(
+                        label="Hobbies & Interests",
+                        placeholder="e.g., programming, gaming, business, teaching, healthcare...",
+                        info="Describe your interests and hobbies"
+                    )
+                    
+                    recommend_btn = gr.Button("Get Recommendations", variant="primary")
+                
+                with gr.Column():
+                    recommendations_output = gr.Markdown()
+            
+            recommend_btn.click(
+                fn=get_course_recommendations,
+                inputs=[stanine_input, gwa_input, strand_input, hobbies_input],
+                outputs=recommendations_output
+            )
+        
+        # Information Tab
+        with gr.Tab("📚 Information"):
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("### FAQ Section")
+                    faq_btn = gr.Button("Show FAQs")
+                    faq_output = gr.Markdown()
+                    faq_btn.click(fn=get_faqs, outputs=faq_output)
+                
+                with gr.Column():
+                    gr.Markdown("### Available Courses")
+                    courses_btn = gr.Button("Show Courses")
+                    courses_output = gr.Markdown()
+                    courses_btn.click(fn=get_available_courses, outputs=courses_output)
+
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True
+    )