Update app.py

app.py

@@ -1,234 +1,240 @@
+import os
 import gradio as gr
-from chatbot import Chatbot
-import json
+import pandas as pd
+import numpy as np
+from ai_chatbot import AIChatbot
+from database_recommender import CourseRecommender
+import warnings
+import logging
 
-# Initialize chatbot
-chatbot = Chatbot()
+# Suppress warnings
+warnings.filterwarnings('ignore')
+logging.getLogger('tensorflow').setLevel(logging.ERROR)
 
-def chat_interface(message, history):
-    """Handle chat interface with Gradio"""
+# 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 message."
+        return "Please enter a message to start the conversation."
     
-    # Get response from chatbot
-    response = chatbot.get_response(message)
+    # Get answer from chatbot
+    answer, confidence = chatbot.find_best_match(message)
     
-    # Format the response for display (removed confidence)
-    if response['status'] == 'success':
-        formatted_response = response['answer']
-    else:
-        formatted_response = response['answer']
+    # For general conversation, just return the answer
+    # For FAQ questions, include suggested questions
+    if confidence > 0.7:  # High confidence FAQ match
+        suggested_questions = chatbot.get_suggested_questions(message)
+        if suggested_questions:
+            response = f"{answer}\n\n**Related Questions:**\n"
+            for i, q in enumerate(suggested_questions, 1):
+                response += f"{i}. {q}\n"
+            return response
     
-    return formatted_response
-
-def get_system_info():
-    """Get system information"""
-    faq_count = chatbot.get_qa_count()
-    return f"System Status: ✅ Active\nFAQ Pairs Loaded: {faq_count}\nCourse Recommender: ✅ Ready"
+    # For general conversation or low confidence, just return the answer
+    return answer
 
 def get_course_recommendations(stanine, gwa, strand, hobbies):
     """Get course recommendations"""
-    return chatbot.get_course_recommendations(stanine, gwa, strand, hobbies)
+    if recommender is None:
+        return "Sorry, the recommendation system is not available at the moment. Please try again later."
+    
+    try:
+        # Validate and convert inputs
+        try:
+            stanine = int(stanine.strip()) if stanine else 0
+        except (ValueError, AttributeError):
+            return "❌ Stanine score must be a valid number between 1 and 9"
+        
+        try:
+            gwa = float(gwa.strip()) if gwa else 0
+        except (ValueError, AttributeError):
+            return "❌ GWA must be a valid number between 75 and 100"
+        
+        # Validate ranges
+        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 or 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="AI Chatbot",
-    theme=gr.themes.Soft(),
-    css="""
-    .gradio-container {
-        max-width: 800px !important;
-        margin: auto !important;
-    }
-    .chat-message {
-        padding: 10px;
-        margin: 5px 0;
-        border-radius: 10px;
-    }
-    """
-) as demo:
-    
+with gr.Blocks(title="PSAU AI Chatbot & Course Recommender", theme=gr.themes.Soft()) as demo:
     gr.Markdown(
         """
-        # 🤖 AI Student Assistant
+        # 🤖 PSAU AI Chatbot & Course Recommender
         
-        Get answers to your questions and personalized course recommendations!
-        
-        **Features:**
-        - FAQ Chat: Get instant answers from our knowledge base
-        - Course Recommendations: Get personalized course suggestions based on your profile
+        Welcome to the Pangasinan State University AI-powered admission assistant! 
+        Get instant answers to your questions and receive personalized course recommendations.
         """
     )
     
     with gr.Tabs():
-        with gr.TabItem("💬 FAQ Chat"):
-            with gr.Row():
-                with gr.Column(scale=3):
-                    chatbot_interface = gr.Chatbot(
-                        label="FAQ Chat",
-                        height=400,
-                        show_label=True,
-                        container=True,
-                        bubble_full_width=False
-                    )
-                    
-                    with gr.Row():
-                        msg = gr.Textbox(
-                            placeholder="Type your question here...",
-                            show_label=False,
-                            scale=4,
-                            container=False
-                        )
-                        submit_btn = gr.Button("Send", variant="primary", scale=1)
-                
-                with gr.Column(scale=1):
-                    gr.Markdown("### System Info")
-                    system_info = gr.Textbox(
-                        value=get_system_info(),
-                        label="Status",
-                        interactive=False,
-                        lines=4
-                    )
-                    
-                    refresh_btn = gr.Button("Refresh Status", variant="secondary")
-                    
-                    gr.Markdown("### FAQ Instructions")
-                    gr.Markdown(
-                        """
-                        **How to use:**
-                        1. Type your question in the text box
-                        2. Click Send or press Enter
-                        3. Get AI-powered answers from FAQ database
-                        
-                        **Tips:**
-                        - Ask specific questions for better results
-                        - Try rephrasing if you don't get a good match
-                        """
-                    )
+        # Chatbot Tab
+        with gr.Tab("🤖 AI Chatbot"):
+            gr.Markdown("""
+            **Chat with the PSAU AI Assistant!**
+            
+            I can help you with:
+            • University admission questions
+            • Course information and guidance  
+            • General conversation
+            • Academic support
+            
+            Just type your message below and I'll respond naturally!
+            """)
+            
+            chatbot_interface = gr.ChatInterface(
+                fn=chat_with_bot,
+                title="PSAU AI Assistant",
+                description="Chat with me about university admissions, courses, or just say hello!",
+                examples=[
+                    "Hello!",
+                    "What are the admission requirements?",
+                    "How are you?",
+                    "What courses are available?",
+                    "Tell me about PSAU",
+                    "What can you help me with?",
+                    "Thank you",
+                    "Goodbye"
+                ],
+                cache_examples=True
+            )
         
-        with gr.TabItem("🎯 Course Recommendations"):
+        # Course Recommender Tab
+        with gr.Tab("🎯 Course Recommender"):
+            gr.Markdown("""
+            Get personalized course recommendations based on your academic profile and interests!
+            
+            **Input Guidelines:**
+            - **Stanine Score**: Enter a number between 1-9 (from your entrance exam)
+            - **GWA**: Enter your General Weighted Average (75-100)
+            - **Strand**: Select your senior high school strand
+            - **Hobbies**: Describe your interests and hobbies in detail
+            """)
+            
             with gr.Row():
-                with gr.Column(scale=2):
-                    gr.Markdown("### 📝 Student Profile")
-                    
-                    stanine_input = gr.Slider(
-                        minimum=1,
-                        maximum=9,
-                        step=1,
-                        value=5,
+                with gr.Column():
+                    stanine_input = gr.Textbox(
                         label="Stanine Score (1-9)",
-                        info="Your stanine score from standardized tests"
+                        placeholder="Enter your stanine score (1-9)",
+                        info="Your stanine score from entrance examination",
+                        value="7"
                     )
-                    
-                    gwa_input = gr.Slider(
-                        minimum=75,
-                        maximum=100,
-                        step=0.1,
-                        value=85,
+                    gwa_input = gr.Textbox(
                         label="GWA (75-100)",
-                        info="Your General Weighted Average"
+                        placeholder="Enter your GWA (75-100)",
+                        info="Your General Weighted Average",
+                        value="85.0"
                     )
-                    
                     strand_input = gr.Dropdown(
                         choices=["STEM", "ABM", "HUMSS"],
-                        label="Senior High School Strand",
-                        info="Select your SHS strand"
+                        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...",
-                        lines=3,
+                        placeholder="e.g., programming, gaming, business, teaching, healthcare...",
                         info="Describe your interests and hobbies"
                     )
                     
-                    recommend_btn = gr.Button("Get Recommendations", variant="primary", size="lg")
+                    recommend_btn = gr.Button("Get Recommendations", variant="primary")
                 
-                with gr.Column(scale=3):
-                    gr.Markdown("### 🎯 Your Course Recommendations")
-                    recommendations_output = gr.Markdown(
-                        value="Enter your profile details and click 'Get Recommendations' to see personalized course suggestions.",
-                        label="Recommendations"
-                    )
-                    
-                    gr.Markdown("### 📚 Available Courses")
-                    gr.Markdown(
-                        """
-                        **STEM Courses:**
-                        - BSCS: Bachelor of Science in Computer Science
-                        - BSIT: Bachelor of Science in Information Technology
-                        - BSArch: Bachelor of Science in Architecture
-                        - BSIE: Bachelor of Science in Industrial Engineering
-                        - BSN: Bachelor of Science in Nursing
-                        
-                        **ABM Courses:**
-                        - BSBA: Bachelor of Science in Business Administration
-                        - BSA: Bachelor of Science in Accountancy
-                        
-                        **HUMSS Courses:**
-                        - BSED: Bachelor of Science in Education
-                        - BSPsych: Bachelor of Science in Psychology
-                        
-                        **Other Courses:**
-                        - BSHM: Bachelor of Science in Hospitality Management
-                        - BSAgri: Bachelor of Science in Agriculture
-                        """
-                    )
-    
-    # Event handlers
-    def user(user_message, history):
-        return "", history + [[user_message, None]]
-    
-    def bot(history):
-        user_message = history[-1][0]
-        bot_message = chat_interface(user_message, history)
-        history[-1][1] = bot_message
-        return history
-    
-    def refresh_system_info():
-        return get_system_info()
-    
-    # Connect FAQ Chat events
-    submit_btn.click(
-        fn=user,
-        inputs=[msg, chatbot_interface],
-        outputs=[msg, chatbot_interface],
-        queue=False
-    ).then(
-        fn=bot,
-        inputs=chatbot_interface,
-        outputs=chatbot_interface,
-        queue=True
-    )
-    
-    msg.submit(
-        fn=user,
-        inputs=[msg, chatbot_interface],
-        outputs=[msg, chatbot_interface],
-        queue=False
-    ).then(
-        fn=bot,
-        inputs=chatbot_interface,
-        outputs=chatbot_interface,
-        queue=True
-    )
-    
-    # Connect Course Recommendation events
-    recommend_btn.click(
-        fn=get_course_recommendations,
-        inputs=[stanine_input, gwa_input, strand_input, hobbies_input],
-        outputs=recommendations_output
-    )
-    
-    refresh_btn.click(
-        fn=refresh_system_info,
-        outputs=system_info
-    )
+                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,
-        quiet=False
-    )
+        show_error=True
+    )