Update app.py

app.py

@@ -2,22 +2,32 @@ import gradio as gr
 import numpy as np
 import os
 
-print("Loading TensorFlow and Keras Hub...")
-import tensorflow as tf
-from tensorflow import keras
-import keras_hub
-print(f"TensorFlow version: {tf.__version__}")
-print(f"Keras Hub version: {keras_hub.__version__}")
+# 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
 
-# Load the BERT model
-print("Loading BERT model...")
-try:
-    model = keras.models.load_model('model_4.keras')
-    print("Model loaded successfully!")
-    print(f"Model type: {type(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"""
@@ -29,14 +39,13 @@ def predict_disaster(text):
     
     try:
         # BERT model directly accepts raw text (has built-in preprocessing)
-        # Make prediction
         prediction = model.predict([text], verbose=0)[0][0]
         
-        # Since the model uses sigmoid activation, prediction is already a probability
+        # Calculate probabilities
         disaster_prob = float(prediction)
         not_disaster_prob = 1 - disaster_prob
         
-        # Determine result message
+        # Result message
         if disaster_prob > 0.5:
             result = f"🚨 **DISASTER** (Confidence: {disaster_prob*100:.1f}%)"
         else:
@@ -74,17 +83,15 @@ examples = [
 
 # 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! 🚑🔥🌊
-        """
-    )
+    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):
@@ -94,8 +101,9 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Disaster Tweet Classifier") as dem
                 lines=4
             )
             
-            predict_btn = gr.Button("🔍 Classify Tweet", variant="primary", size="lg")
-            clear_btn = gr.Button("🗑️ Clear", size="sm")
+            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(
@@ -106,8 +114,11 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Disaster Tweet Classifier") as dem
     output_text = gr.Markdown(label="Result")
     
     # Examples section
-    gr.Markdown("### 📝 Try These Examples:")
-    gr.Markdown("Click on any example below to automatically classify it")
+    gr.Markdown("""
+    ---
+    ### 📝 Try These Examples:
+    Click on any example below to automatically classify it
+    """)
     
     gr.Examples(
         examples=examples,
@@ -118,6 +129,36 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Disaster Tweet Classifier") as dem
         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
+    
+    ---
+    
+    **Created by**: berkeruveyik | **Model**: BERT Tiny | **Framework**: TensorFlow + Keras Hub
+    """)
+    
     # Event handlers
     predict_btn.click(
         fn=predict_disaster,
@@ -135,39 +176,10 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Disaster Tweet Classifier") as dem
         fn=lambda: ("", {"Disaster": 0.0, "Not Disaster": 0.0}, ""),
         outputs=[input_text, output_label, output_text]
     )
-    
-    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
-        
-        ---
-        
-        **Created by**: berkeruveyik | **Model**: BERT Tiny | **Framework**: TensorFlow + Keras Hub
-        """
-    )
 
 # Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        share=False,
+        debug=False
+    )