app.py

import gradio as gr
import numpy as np
import os

# Lazy import - sadece model yüklerken import et
def load_model():
    """Model ve gerekli kütüphaneleri lazy loading ile yükle"""
    print("Loading TensorFlow...")
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # TensorFlow loglarını azalt
    
    import tensorflow as tf
    print(f"TensorFlow version: {tf.__version__}")
    
    print("Loading Keras Hub...")
    import keras_hub
    print(f"Keras Hub version: {keras_hub.__version__}")
    
    print("Loading BERT model...")
    try:
        model = tf.keras.models.load_model('model_4.keras')
        print("✅ Model loaded successfully!")
        return model
    except Exception as e:
        print(f"❌ Error loading model: {e}")
        raise

# Model yükleme - global değişken
print("Initializing application...")
model = load_model()
print("Application ready!")

def predict_disaster(text):
    """Predict if a tweet is about a disaster or not"""
    if not text.strip():
        return {
            "Disaster": 0.0,
            "Not Disaster": 0.0
        }, "⚠️ Please enter a tweet to classify"
    
    try:
        # BERT model directly accepts raw text (has built-in preprocessing)
        prediction = model.predict([text], verbose=0)[0][0]
        
        # Calculate probabilities
        disaster_prob = float(prediction)
        not_disaster_prob = 1 - disaster_prob
        
        # Result message
        if disaster_prob > 0.5:
            result = f"🚨 **DISASTER** (Confidence: {disaster_prob*100:.1f}%)"
        else:
            result = f"✅ **NOT DISASTER** (Confidence: {not_disaster_prob*100:.1f}%)"
        
        return {
            "Disaster": disaster_prob,
            "Not Disaster": not_disaster_prob
        }, result
    
    except Exception as e:
        return {
            "Disaster": 0.0,
            "Not Disaster": 0.0
        }, f"❌ Error during prediction: {str(e)}"

# Example tweets for testing
examples = [
    ["Our Deeds are the Reason of this #earthquake May ALLAH Forgive us all"],
    ["Forest fire near La Ronge Sask. Canada"],
    ["13,000 people receive #wildfires evacuation orders in California"],
    ["Just happened a terrible car crash"],
    ["I love summer days at the beach with friends"],
    ["The sunset today is absolutely beautiful"],
    ["Residents asked to shelter in place are being notified by officers. No other evacuation or shelter in place orders are expected"],
    ["This is so awesome! Best day ever!"],
    ["Heard loud noises from downtown, seems like an explosion"],
    ["I'm making dinner tonight, trying a new recipe"],
    ["Buildings are collapsing after the earthquake"],
    ["Had a great time at the party last night!"],
    ["Emergency services responding to massive flooding in the area"],
    ["Can't wait for the weekend to start"],
    ["Tornado warning issued for our county, take shelter immediately"]
]

# Create Gradio interface
with gr.Blocks(theme=gr.themes.Soft(), title="Disaster Tweet Classifier") as demo:
    gr.Markdown("""
    # 🚨 Disaster Tweet Classification
    ### AI-Powered BERT Model to Identify Real Disaster Reports
    
    This application uses a fine-tuned **BERT** (Bidirectional Encoder Representations from Transformers) model 
    to analyze tweets and classify them as either referring to a **real disaster** or **not a disaster**.
    
    Perfect for emergency response teams, news organizations, and disaster management agencies! 🚑🔥🌊
    """)
    
    with gr.Row():
        with gr.Column(scale=2):
            input_text = gr.Textbox(
                label="📝 Enter Tweet Text",
                placeholder="Type or paste a tweet here... (e.g., 'Earthquake hits California')",
                lines=4
            )
            
            with gr.Row():
                clear_btn = gr.Button("🗑️ Clear", variant="secondary")
                predict_btn = gr.Button("🔍 Classify Tweet", variant="primary", size="lg")
            
        with gr.Column(scale=1):
            output_label = gr.Label(
                label="📊 Prediction Confidence",
                num_top_classes=2
            )
    
    output_text = gr.Markdown(label="Result")
    
    # Examples section
    gr.Markdown("""
    ---
    ### 📝 Try These Examples:
    Click on any example below to automatically classify it
    """)
    
    gr.Examples(
        examples=examples,
        inputs=input_text,
        outputs=[output_label, output_text],
        fn=predict_disaster,
        cache_examples=False,
        label="Sample Tweets"
    )
    
    gr.Markdown("""
    ---
    ### ℹ️ About This Model
    
    **Model Architecture**: BERT Tiny (English, Uncased)
    - **Parameters**: ~4.4M parameters
    - **Training**: Fine-tuned on disaster tweet dataset
    - **Accuracy**: Optimized for real-time disaster detection
    
    **Use Cases**:
    - 🚨 Emergency response monitoring
    - 📰 News verification
    - 🌐 Social media analysis
    - 🔍 Crisis management
    
    **How it Works**:
    The model uses contextual understanding to distinguish between:
    - Real disaster reports (earthquakes, fires, accidents, floods, etc.)
    - Casual language or metaphorical usage of disaster-related words
    
    **Limitations**:
    - Optimized for English tweets only
    - May require context for ambiguous cases
    - Should be used as a support tool, not sole decision-maker
    

    """)
    
    # Event handlers
    predict_btn.click(
        fn=predict_disaster,
        inputs=input_text,
        outputs=[output_label, output_text]
    )
    
    input_text.submit(
        fn=predict_disaster,
        inputs=input_text,
        outputs=[output_label, output_text]
    )
    
    clear_btn.click(
        fn=lambda: ("", {"Disaster": 0.0, "Not Disaster": 0.0}, ""),
        outputs=[input_text, output_label, output_text]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch(
        share=False,
        debug=False
    )