app.py

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
    )