Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+import pickle
+import re
+
+# Load the model
+model = keras.models.load_model('model_4.keras')
+
+# Load tokenizer (you need to upload tokenizer.pickle to your Space)
+try:
+    with open('tokenizer.pickle', 'rb') as handle:
+        tokenizer = pickle.load(handle)
+except:
+    print("Warning: Tokenizer not found. Creating a basic one.")
+    from tensorflow.keras.preprocessing.text import Tokenizer
+    tokenizer = Tokenizer(num_words=10000, oov_token="<OOV>")
+
+# Get max sequence length from model input shape
+max_length = model.input_shape[1] if len(model.input_shape) > 1 else 100
+
+def preprocess_text(text):
+    """Clean and preprocess the tweet text"""
+    # Convert to lowercase
+    text = text.lower()
+    # Remove URLs
+    text = re.sub(r'http\S+|www\S+|https\S+', '', text, flags=re.MULTILINE)
+    # Remove user mentions and hashtags
+    text = re.sub(r'\@\w+|\#','', text)
+    # Remove extra spaces
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text
+
+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"
+    
+    # Preprocess the text
+    processed_text = preprocess_text(text)
+    
+    # Tokenize and pad
+    sequences = tokenizer.texts_to_sequences([processed_text])
+    padded = keras.preprocessing.sequence.pad_sequences(
+        sequences, 
+        maxlen=max_length, 
+        padding='post', 
+        truncating='post'
+    )
+    
+    # Make prediction
+    prediction = model.predict(padded, verbose=0)[0][0]
+    
+    # Create confidence scores
+    disaster_prob = float(prediction)
+    not_disaster_prob = 1 - disaster_prob
+    
+    # Determine result message
+    if disaster_prob > 0.5:
+        result = f"🚨 **DISASTER** (Confidence: {disaster_prob*100:.1f}%)"
+        emoji = "🚨"
+    else:
+        result = f"✅ **NOT DISASTER** (Confidence: {not_disaster_prob*100:.1f}%)"
+        emoji = "✅"
+    
+    return {
+        "Disaster": disaster_prob,
+        "Not Disaster": not_disaster_prob
+    }, result
+
+# 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!"],
+    ["Bombing at the airport, many casualties reported"],
+    ["I'm making dinner tonight, trying a new recipe"]
+]
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft(), title="Disaster Tweet Classifier") as demo:
+    gr.Markdown(
+        """
+        # 🚨 Disaster Tweet Classification
+        ### Determine whether a tweet is about a real disaster or not
+        
+        This AI model analyzes tweets and classifies them as either referring to a **real disaster** (earthquake, fire, accident, etc.) 
+        or **not a disaster** (regular conversation, metaphorical usage).
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            input_text = gr.Textbox(
+                label="Enter Tweet Text",
+                placeholder="Type or paste a tweet here...",
+                lines=3
+            )
+            
+            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:")
+    gr.Examples(
+        examples=examples,
+        inputs=input_text,
+        outputs=[output_label, output_text],
+        fn=predict_disaster,
+        cache_examples=False
+    )
+    
+    # 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]
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        ### ℹ️ About
+        This model uses Natural Language Processing to classify disaster-related tweets. 
+        It can help emergency services and news organizations quickly identify real disaster reports.
+        """
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()