Added files for application

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+
+# Load the trained model and preprocessing objects
+model = joblib.load('exoplanet_model.pkl')
+scaler = joblib.load('scaler.pkl')
+feature_names = joblib.load('feature_names.pkl')
+
+def predict_exoplanet(period, time0bk, impact, duration, depth, snr, prad, 
+                      eqt, insol, steff, slogg, sradius):
+    """
+    Predict if the input represents an exoplanet or false positive
+    """
+    try:
+        # Create input dictionary
+        input_data = {
+            'tce_period': float(period),
+            'tce_time0bk': float(time0bk),
+            'tce_impact': float(impact),
+            'tce_duration': float(duration),
+            'tce_depth': float(depth),
+            'tce_model_snr': float(snr),
+            'tce_prad': float(prad),
+            'tce_eqt': float(eqt),
+            'tce_insol': float(insol),
+            'tce_steff': float(steff),
+            'tce_slogg': float(slogg),
+            'tce_sradius': float(sradius)
+        }
+        
+        # Create DataFrame
+        df = pd.DataFrame([input_data])
+        
+        # Add engineered features
+        df['temp_radius_ratio'] = df['tce_steff'] / (df['tce_sradius'] + 1e-6)
+        df['period_duration_ratio'] = df['tce_period'] / (df['tce_duration'] + 1e-6)
+        df['snr_depth_product'] = df['tce_model_snr'] * df['tce_depth']
+        
+        # Ensure correct feature order
+        df = df[feature_names]
+        
+        # Scale features
+        df_scaled = scaler.transform(df)
+        
+        # Make prediction
+        prediction = model.predict(df_scaled)[0]
+        
+        # Get probability if available
+        if hasattr(model, 'predict_proba'):
+            probabilities = model.predict_proba(df_scaled)[0]
+            confidence = probabilities[prediction]
+            prob_exoplanet = probabilities[1]
+            prob_false_positive = probabilities[0]
+        else:
+            confidence = None
+            prob_exoplanet = prediction
+            prob_false_positive = 1 - prediction
+        
+        # Format result
+        result = "🪐 EXOPLANET DETECTED" if prediction == 1 else "❌ FALSE POSITIVE"
+        
+        # Create detailed output
+        details = f"""
+        ### Prediction: {result}
+        
+        **Confidence:** {confidence:.2%} if confidence else 'N/A'
+        
+        **Probability Breakdown:**
+        - Exoplanet: {prob_exoplanet:.2%}
+        - False Positive: {prob_false_positive:.2%}
+        
+        **Input Summary:**
+        - Orbital Period: {period} days
+        - Transit Depth: {depth} ppm
+        - Signal-to-Noise Ratio: {snr}
+        - Planet Radius: {prad} Earth radii
+        - Stellar Temperature: {steff} K
+        """
+        
+        return details
+        
+    except Exception as e:
+        return f"Error: {str(e)}\n\nPlease check your input values."
+
+# Example datasets for quick testing
+examples = [
+    # Confirmed exoplanet-like parameters
+    [3.5, 135.0, 0.3, 2.5, 800, 20.0, 2.0, 450, 100, 5800, 4.5, 1.0],
+    # False positive-like parameters
+    [50.0, 200.0, 0.9, 15.0, 50, 5.0, 15.0, 300, 5, 4500, 4.8, 0.5],
+    # Hot Jupiter-like
+    [0.8, 140.0, 0.1, 1.2, 15000, 50.0, 11.0, 1500, 5000, 6200, 4.3, 1.2],
+]
+
+# Create Gradio interface
+with gr.Blocks(title="Exoplanet Detection System", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🌌 Exoplanet Detection System
+        
+        This AI-powered system predicts whether a transit signal represents a genuine exoplanet 
+        or a false positive based on Kepler space telescope data.
+        
+        ### How to use:
+        1. Enter the transit and stellar parameters below
+        2. Click "Detect Exoplanet" to get prediction
+        3. Or try example cases for quick testing
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Transit Parameters")
+            period = gr.Number(label="Orbital Period (days)", value=10.0, 
+                             info="Time between successive transits")
+            time0bk = gr.Number(label="Transit Epoch (BJD - 2454833)", value=135.0,
+                              info="Time of first transit")
+            impact = gr.Slider(0, 1, value=0.5, label="Impact Parameter",
+                             info="0 = center crossing, 1 = grazing")
+            duration = gr.Number(label="Transit Duration (hours)", value=3.0,
+                               info="How long the transit lasts")
+            depth = gr.Number(label="Transit Depth (ppm)", value=500.0,
+                            info="Fractional decrease in brightness")
+            snr = gr.Number(label="Signal-to-Noise Ratio", value=15.0,
+                          info="Quality of the detection")
+            
+        with gr.Column():
+            gr.Markdown("### Planet & Stellar Parameters")
+            prad = gr.Number(label="Planet Radius (Earth radii)", value=2.5,
+                           info="Size relative to Earth")
+            eqt = gr.Number(label="Equilibrium Temperature (K)", value=400.0,
+                          info="Estimated planet temperature")
+            insol = gr.Number(label="Insolation (Earth flux)", value=50.0,
+                            info="Stellar flux received")
+            steff = gr.Number(label="Stellar Temperature (K)", value=5500.0,
+                            info="Host star temperature")
+            slogg = gr.Slider(2, 5, value=4.5, label="Stellar Surface Gravity (log g)",
+                            info="Indicates star type")
+            sradius = gr.Number(label="Stellar Radius (Solar radii)", value=1.0,
+                              info="Size of host star")
+    
+    detect_btn = gr.Button("🔍 Detect Exoplanet", variant="primary", size="lg")
+    
+    output = gr.Markdown(label="Prediction Results")
+    
+    detect_btn.click(
+        fn=predict_exoplanet,
+        inputs=[period, time0bk, impact, duration, depth, snr, prad, 
+                eqt, insol, steff, slogg, sradius],
+        outputs=output
+    )
+    
+    gr.Markdown("### 📋 Example Cases")
+    gr.Examples(
+        examples=examples,
+        inputs=[period, time0bk, impact, duration, depth, snr, prad, 
+                eqt, insol, steff, slogg, sradius],
+        label="Try these example transit signals"
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        **Model Information:**
+        - Trained on Kepler mission data
+        - Uses ensemble machine learning techniques
+        - Features: Transit timing, depth, stellar parameters, and more
+        
+        **Note:** This is a demonstration tool. Real exoplanet confirmation requires 
+        extensive follow-up observations and validation.
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)

exoplanet_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aca2cbf147a8c094739a8d81f0f7045e60d13e72d5101fc9cfed6ecad43d9d74
+size 2352745

feature_names.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de57c547dc2182d5a1e9db0520bc864614a8bd7eb46fa9123988e6bb39efbb5d
+size 234

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:618ad53cbbe70776585762d4dffdc9461372a68278424fee7f5fe07000d55806
+size 1519