app.py

import os
import gradio as gr
import pandas as pd
import numpy as np
from ai_chatbot import AIChatbot, chat_with_bot_via_gateway, get_faqs_via_gateway
from database_recommender import CourseRecommender, get_course_recommendations_ui
import warnings
import logging
import requests

# 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

GATEWAY_BASE_URL = os.environ.get('GATEWAY_BASE_URL', 'https://database-46m3.onrender.com')

def chat_with_bot(message, history):
    """Handle chatbot interactions via gateway-aware helper"""
    return chat_with_bot_via_gateway(chatbot, message, history, gateway_base_url=GATEWAY_BASE_URL)

def get_course_recommendations(stanine, gwa, strand, hobbies):
    return get_course_recommendations_ui(recommender, stanine, gwa, strand, hobbies)

def get_faqs():
    return get_faqs_via_gateway(gateway_base_url=GATEWAY_BASE_URL)

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("""
            **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
            )
        
        # 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():
                    stanine_input = gr.Textbox(
                        label="Stanine Score (1-9)",
                        placeholder="Enter your stanine score (1-9)",
                        info="Your stanine score from entrance examination",
                        value="7"
                    )
                    gwa_input = gr.Textbox(
                        label="GWA (75-100)",
                        placeholder="Enter your GWA (75-100)",
                        info="Your General Weighted Average",
                        value="85.0"
                    )
                    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
    )