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soho-comet-detector

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MohammedSameerSyed commited on Oct 8, 2025

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1a4b0b8

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1 Parent(s): 6e9e30c

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app.py +331 -456

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@@ -10,9 +10,7 @@ import zipfile
 import tempfile
 from pathlib import Path
 import cv2
-import requests
-from datetime import datetime, timedelta
-import json
 import base64
 import io
@@ -28,15 +26,16 @@ class CometDetectorAPI:
         ])
     def fetch_soho_recent(self):
-        """Fetch recent SOHO data (demo implementation)"""
-        try:
-            images = []
-            for i in range(12):
-                img = np.random.rand(1024, 1024) * 50 + np.random.rand(1024, 1024) * 50
-                images.append(img.astype(np.float32))
-            return np.array(images), "Live SOHO Data (Demo)"
-        except Exception as e:
-            return None, str(e)
     def load_fits_from_zip(self, zip_file):
         images = []
@@ -77,7 +76,10 @@ class CometDetectorAPI:
             confidence = probs[0][pred_class].item()
         return pred_class, confidence
-    def generate_visualization(self, images, max_proj, pred_class, confidence):
         plt.style.use('dark_background')
         fig, axes = plt.subplots(1, 2, figsize=(14, 6))
         fig.patch.set_facecolor('#0a0e27')
@@ -101,312 +103,37 @@ class CometDetectorAPI:
         img_base64 = base64.b64encode(buf.read()).decode()
         plt.close()
-        return f"data:image/png;base64,{img_base64}"
-    def analyze_uploaded(self, zip_file):
-        if zip_file is None:
-            return {"error": "No file uploaded"}
-        try:
-            images = self.load_fits_from_zip(zip_file.name)
-            if len(images) < 2:
-                return {"error": "Need at least 2 FITS images"}
-            max_proj = self.create_difference_images(images)
-            pred_class, confidence = self.classify_image(max_proj)
-            img_data = self.generate_visualization(images, max_proj, pred_class, confidence)
-            return {
-                "success": True,
-                "detected": bool(pred_class),
-                "confidence": float(confidence),
-                "num_images": len(images),
-                "source": "Uploaded Data",
-                "timestamp": datetime.utcnow().isoformat(),
-                "image": img_data
-            }
-        except Exception as e:
-            return {"error": str(e)}
-    def analyze_soho_live(self):
-        try:
-            images, source = self.fetch_soho_recent()
-            if images is None:
-                return {"error": "Failed to fetch SOHO data"}
-            max_proj = self.create_difference_images(images)
-            pred_class, confidence = self.classify_image(max_proj)
-            img_data = self.generate_visualization(images, max_proj, pred_class, confidence)
-            return {
-                "success": True,
-                "detected": bool(pred_class),
-                "confidence": float(confidence),
-                "num_images": len(images),
-                "source": source,
-                "timestamp": datetime.utcnow().isoformat(),
-                "image": img_data
-            }
-        except Exception as e:
-            return {"error": str(e)}
-detector = CometDetectorAPI('best_model.pth')
-custom_html = """
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>COMET-SEE Mission Control</title>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r128/three.min.js"></script>
-    <style>
-        @import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;700;900&family=Share+Tech+Mono&display=swap');
-        * { margin: 0; padding: 0; box-sizing: border-box; }
-        body { font-family: 'Share Tech Mono', monospace; background: #000; color: #fff; overflow-x: hidden; }
-        #canvas-container { position: fixed; top: 0; left: 0; width: 100%; height: 100%; z-index: -1; }
-        .container { position: relative; z-index: 1; max-width: 1400px; margin: 0 auto; padding: 20px; }
-        .header {
-            text-align: center; padding: 40px 20px;
-            background: linear-gradient(135deg, rgba(10, 14, 39, 0.9) 0%, rgba(0, 20, 40, 0.8) 100%);
-            border-radius: 20px; border: 2px solid rgba(0, 217, 255, 0.3);
-            box-shadow: 0 0 40px rgba(0, 217, 255, 0.2); margin-bottom: 30px;
-            backdrop-filter: blur(10px);
-        }
-        .title {
-            font-family: 'Orbitron', sans-serif; font-size: 4em; font-weight: 900;
-            background: linear-gradient(135deg, #00d9ff 0%, #00ff88 50%, #ff00ff 100%);
-            -webkit-background-clip: text; -webkit-text-fill-color: transparent;
-            text-shadow: 0 0 30px rgba(0, 217, 255, 0.5); margin-bottom: 10px;
-            animation: glow 2s ease-in-out infinite alternate;
-        }
-        @keyframes glow {
-            from { filter: drop-shadow(0 0 5px rgba(0, 217, 255, 0.5)); }
-            to { filter: drop-shadow(0 0 20px rgba(0, 217, 255, 0.8)); }
-        }
-        .subtitle {
-            font-size: 1.2em; color: #00d9ff; letter-spacing: 3px; margin-bottom: 20px;
-        }
-        .team { font-size: 1.1em; color: #00ff88; margin-top: 15px; }
-        .team-member {
-            display: inline-block; padding: 5px 15px; margin: 0 10px;
-            background: rgba(0, 255, 136, 0.1); border: 1px solid #00ff88;
-            border-radius: 20px; font-weight: bold;
-            animation: pulse 2s ease-in-out infinite;
-        }
-        @keyframes pulse {
-            0%, 100% { transform: scale(1); }
-            50% { transform: scale(1.05); }
-        }
-        .control-panel { display: grid; grid-template-columns: 1fr 1fr; gap: 30px; margin-bottom: 30px; }
-        .panel {
-            background: linear-gradient(135deg, rgba(10, 14, 39, 0.95) 0%, rgba(0, 20, 40, 0.9) 100%);
-            border-radius: 15px; padding: 30px; border: 2px solid rgba(0, 217, 255, 0.3);
-            backdrop-filter: blur(10px); box-shadow: 0 8px 32px rgba(0, 0, 0, 0.3);
-            transition: all 0.3s ease;
-        }
-        .panel:hover {
-            border-color: rgba(0, 217, 255, 0.6);
-            box-shadow: 0 8px 40px rgba(0, 217, 255, 0.3);
-            transform: translateY(-5px);
-        }
-        .panel-title {
-            font-family: 'Orbitron', sans-serif; font-size: 1.5em; color: #00d9ff;
-            margin-bottom: 20px; text-transform: uppercase; letter-spacing: 2px;
-        }
-        .upload-zone {
-            border: 3px dashed rgba(0, 217, 255, 0.5); border-radius: 10px;
-            padding: 40px; text-align: center; cursor: pointer;
-            transition: all 0.3s ease; background: rgba(0, 217, 255, 0.05);
-        }
-        .upload-zone:hover {
-            border-color: #00d9ff; background: rgba(0, 217, 255, 0.1);
-        }
-        .upload-icon { font-size: 3em; margin-bottom: 15px; }
-        input[type="file"] { display: none; }
-        .btn {
-            font-family: 'Orbitron', sans-serif; padding: 15px 40px; font-size: 1.1em;
-            border: none; border-radius: 10px; cursor: pointer; text-transform: uppercase;
-            font-weight: bold; letter-spacing: 2px; transition: all 0.3s ease;
-            margin: 10px; position: relative; overflow: hidden;
-        }
-        .btn-primary {
-            background: linear-gradient(135deg, #00d9ff 0%, #00ff88 100%); color: #000;
-        }
-        .btn-secondary {
-            background: linear-gradient(135deg, #ff00ff 0%, #ff0080 100%); color: #fff;
-        }
-        .btn:hover {
-            transform: translateY(-3px); box-shadow: 0 10px 30px rgba(0, 217, 255, 0.5);
-        }
-        .results {
-            background: linear-gradient(135deg, rgba(10, 14, 39, 0.95) 0%, rgba(0, 20, 40, 0.9) 100%);
-            border-radius: 15px; padding: 30px; border: 2px solid rgba(0, 217, 255, 0.3);
-            backdrop-filter: blur(10px); display: none; animation: slideIn 0.5s ease;
-        }
-        @keyframes slideIn {
-            from { opacity: 0; transform: translateY(20px); }
-            to { opacity: 1; transform: translateY(0); }
-        }
-        .result-detected { border-color: #00ff88; box-shadow: 0 0 40px rgba(0, 255, 136, 0.3); }
-        .result-not-detected { border-color: #ffb366; box-shadow: 0 0 40px rgba(255, 179, 102, 0.3); }
-        .result-header { text-align: center; margin-bottom: 30px; }
-        .result-status { font-family: 'Orbitron', sans-serif; font-size: 2.5em; font-weight: bold; margin-bottom: 10px; }
-        .status-detected { color: #00ff88; text-shadow: 0 0 20px rgba(0, 255, 136, 0.8); }
-        .status-not-detected { color: #ffb366; text-shadow: 0 0 20px rgba(255, 179, 102, 0.8); }
-        .confidence-bar {
-            width: 100%; height: 30px; background: rgba(255, 255, 255, 0.1);
-            border-radius: 15px; overflow: hidden; margin: 20px 0;
-        }
-        .confidence-fill {
-            height: 100%; background: linear-gradient(90deg, #00d9ff 0%, #00ff88 100%);
-            transition: width 1s ease; display: flex; align-items: center;
-            justify-content: center; font-weight: bold; color: #000;
-        }
-        .result-image {
-            width: 100%; border-radius: 10px; margin: 20px 0;
-            border: 2px solid rgba(0, 217, 255, 0.3);
-        }
-        .metadata {
-            display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
-            gap: 15px; margin-top: 20px;
-        }
-        .metadata-item {
-            background: rgba(0, 217, 255, 0.1); padding: 15px; border-radius: 8px;
-            border: 1px solid rgba(0, 217, 255, 0.3);
-        }
-        .metadata-label { color: #00d9ff; font-size: 0.9em; margin-bottom: 5px; }
-        .metadata-value { font-size: 1.2em; font-weight: bold; color: #fff; }
-        .loading { text-align: center; padding: 40px; display: none; }
-        .spinner {
-            border: 4px solid rgba(0, 217, 255, 0.3); border-top: 4px solid #00d9ff;
-            border-radius: 50%; width: 60px; height: 60px;
-            animation: spin 1s linear infinite; margin: 0 auto 20px;
-        }
-        @keyframes spin {
-            0% { transform: rotate(0deg); }
-            100% { transform: rotate(360deg); }
-        }
-        .error {
-            background: rgba(255, 0, 0, 0.1); border: 2px solid rgba(255, 0, 0, 0.5);
-            color: #ff4444; padding: 20px; border-radius: 10px; margin: 20px 0; display: none;
-        }
-        .astronaut {
-            position: fixed; right: 50px; top: 50%; transform: translateY(-50%);
-            font-size: 5em; animation: float 6s ease-in-out infinite; z-index: 999;
-            filter: drop-shadow(0 0 10px rgba(255, 255, 255, 0.5));
-        }
-        @keyframes float {
-            0%, 100% { transform: translateY(-50%) rotate(-5deg); }
-            50% { transform: translateY(calc(-50% - 30px)) rotate(5deg); }
-        }
-        @media (max-width: 768px) {
-            .control-panel { grid-template-columns: 1fr; }
-            .title { font-size: 2.5em; }
-            .astronaut { display: none; }
-        }
-    </style>
-</head>
-<body>
-    <div id="canvas-container"></div>
-    <div class="astronaut">🧑‍🚀</div>
-    <div class="container">
-        <div class="header">
-            <div class="title">COMET-SEE</div>
-            <div class="subtitle">COmet Motion Extraction & Tracking – Statistical Exploration Engine</div>
-            <div class="team">
-                <span class="team-member">👩‍🔬 SHAMBHAVI SRIVASTAVA </span>
-                <span class="team-member">👩‍🚀 EMILY FOLEY</span>
-                <span class="team-member">👨‍💻 MOHAMMED SAMEER SYED</span>
-            </div>
-        </div>
-        <div class="control-panel">
-            <div class="panel">
-                <h2 class="panel-title">📤 Upload FITS Data</h2>
-                <div class="upload-zone" onclick="document.getElementById('fileInput').click()">
-                    <div class="upload-icon">📁</div>
-                    <div>Click to upload ZIP file</div>
-                    <div style="font-size: 0.9em; color: #888; margin-top: 10px;">SOHO LASCO C3 FITS images</div>
-                </div>
-                <input type="file" id="fileInput" accept=".zip" onchange="handleFileUpload(event)">
-                <button class="btn btn-primary" onclick="analyzeUpload()">🔍 Analyze Upload</button>
-            </div>
-            <div class="panel">
-                <h2 class="panel-title">📡 Live SOHO Data</h2>
-                <div style="text-align: center; padding: 20px;">
-                    <div style="font-size: 2em; margin-bottom: 20px;">🛰️</div>
-                    <p style="margin-bottom: 20px; color: #aaa;">Fetch recent SOHO/LASCO C3 images and analyze for comet activity</p>
-                    <button class="btn btn-secondary" onclick="fetchAndAnalyze()">🌐 Fetch Live Data</button>
-                </div>
-            </div>
-        </div>
-        <div class="loading" id="loading">
-            <div class="spinner"></div>
-            <div>Analyzing data...</div>
-        </div>
-        <div class="error" id="error"></div>
-        <div class="results" id="results">
             <div class="result-header">
-                <div class="result-status" id="status"></div>
                 <div class="confidence-bar">
-                    <div class="confidence-fill" id="confidence" style="width: 0%"></div>
                 </div>
             </div>
-            <img class="result-image" id="resultImage" src="" alt="Analysis Result">
             <div class="metadata">
                 <div class="metadata-item">
                     <div class="metadata-label">Images Analyzed</div>
-                    <div class="metadata-value" id="numImages">-</div>
                 </div>
                 <div class="metadata-item">
                     <div class="metadata-label">Data Source</div>
-                    <div class="metadata-value" id="source">-</div>
                 </div>
                 <div class="metadata-item">
                     <div class="metadata-label">Analysis Time</div>
-                    <div class="metadata-value" id="timestamp">-</div>
                 </div>
                 <div class="metadata-item">
                     <div class="metadata-label">Model Accuracy</div>
@@ -414,176 +141,324 @@ custom_html = """
                 </div>
             </div>
         </div>
-    </div>
-    <script>
-        let scene, camera, renderer, stars;
-        function initStarfield() {
-            scene = new THREE.Scene();
-            camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 0.1, 1000);
-            camera.position.z = 5;
-            renderer = new THREE.WebGLRenderer({ alpha: true });
-            renderer.setSize(window.innerWidth, window.innerHeight);
-            document.getElementById('canvas-container').appendChild(renderer.domElement);
-            const starGeometry = new THREE.BufferGeometry();
-            const starMaterial = new THREE.PointsMaterial({ color: 0xffffff, size: 2, transparent: true });
-            const starVertices = [];
-            for (let i = 0; i < 10000; i++) {
-                const x = (Math.random() - 0.5) * 2000;
-                const y = (Math.random() - 0.5) * 2000;
-                const z = (Math.random() - 0.5) * 2000;
-                starVertices.push(x, y, z);
-            }
-            starGeometry.setAttribute('position', new THREE.Float32BufferAttribute(starVertices, 3));
-            stars = new THREE.Points(starGeometry, starMaterial);
-            scene.add(stars);
-            animate();
-        }
-        function animate() {
-            requestAnimationFrame(animate);
-            stars.rotation.y += 0.0002;
-            stars.rotation.x += 0.0001;
-            renderer.render(scene, camera);
-        }
-        window.addEventListener('resize', () => {
-            camera.aspect = window.innerWidth / window.innerHeight;
-            camera.updateProjectionMatrix();
-            renderer.setSize(window.innerWidth, window.innerHeight);
-        });
-        initStarfield();
-        let uploadedFile = null;
-        function handleFileUpload(event) {
-            uploadedFile = event.target.files[0];
-            if (uploadedFile) {
-                document.querySelector('.upload-zone').innerHTML = `
-                    <div class="upload-icon">✅</div>
-                    <div>${uploadedFile.name}</div>
-                    <div style="font-size: 0.9em; color: #00ff88; margin-top: 10px;">Ready to analyze</div>
-                `;
-            }
-        }
-        function showLoading() {
-            document.getElementById('loading').style.display = 'block';
-            document.getElementById('results').style.display = 'none';
-            document.getElementById('error').style.display = 'none';
-        }
-        function showError(message) {
-            document.getElementById('loading').style.display = 'none';
-            document.getElementById('error').textContent = message;
-            document.getElementById('error').style.display = 'block';
-        }
-        function showResults(data) {
-            document.getElementById('loading').style.display = 'none';
-            const results = document.getElementById('results');
-            results.style.display = 'block';
-            if (data.detected) {
-                results.className = 'results result-detected';
-                document.getElementById('status').className = 'result-status status-detected';
-                document.getElementById('status').textContent = '🌟 COMET DETECTED!';
-            } else {
-                results.className = 'results result-not-detected';
-                document.getElementById('status').className = 'result-status status-not-detected';
-                document.getElementById('status').textContent = '🌑 No Comet Activity';
-            }
-            const confidence = Math.round(data.confidence * 100);
-            document.getElementById('confidence').style.width = confidence + '%';
-            document.getElementById('confidence').textContent = confidence + '%';
-            document.getElementById('resultImage').src = data.image;
-            document.getElementById('numImages').textContent = data.num_images;
-            document.getElementById('source').textContent = data.source;
-            document.getElementById('timestamp').textContent = new Date(data.timestamp).toLocaleString();
-            results.scrollIntoView({ behavior: 'smooth' });
-        }
-        async function analyzeUpload() {
-            if (!uploadedFile) {
-                showError('Please upload a ZIP file first');
-                return;
-            }
-            showLoading();
-            const formData = new FormData();
-            formData.append('file', uploadedFile);
-            try {
-                const response = await fetch('/api/analyze_upload', {
-                    method: 'POST',
-                    body: formData
-                });
-                const data = await response.json();
-                if (data.error) {
-                    showError(data.error);
-                } else {
-                    showResults(data);
-                }
-            } catch (error) {
-                showError('Error analyzing file: ' + error.message);
-            }
-        }
-        async function fetchAndAnalyze() {
-            showLoading();
-            try {
-                const response = await fetch('/api/analyze_soho');
-                const data = await response.json();
-                if (data.error) {
-                    showError(data.error);
-                } else {
-                    showResults(data);
-                }
-            } catch (error) {
-                showError('Error fetching SOHO data: ' + error.message);
-            }
-        }
-    </script>
-</body>
-</html>
 """
-with gr.Blocks() as demo:
-    gr.HTML(custom_html)
-    with gr.Row(visible=False):
-        file_input = gr.File()
-        upload_output = gr.JSON()
-        soho_output = gr.JSON()
-        file_input.upload(
-            fn=detector.analyze_uploaded,
-            inputs=[file_input],
-            outputs=[upload_output],
-            api_name="analyze_upload"
-        )
-        gr.Button("Fetch SOHO").click(
-            fn=detector.analyze_soho_live,
-            outputs=[soho_output],
-            api_name="analyze_soho"
-        )
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)

 import tempfile
 from pathlib import Path
 import cv2
+from datetime import datetime
 import base64
 import io
         ])
     def fetch_soho_recent(self):
+        """Generate demo SOHO data"""
+        images = []
+        for i in range(12):
+            # Create synthetic comet-like data with a moving bright spot
+            img = np.random.rand(1024, 1024) * 20
+            # Add a "comet" that moves
+            y, x = 400 + i * 10, 300 + i * 15
+            img[max(0,y-30):min(1024,y+30), max(0,x-30):min(1024,x+30)] += 100
+            images.append(img.astype(np.float32))
+        return np.array(images)
     def load_fits_from_zip(self, zip_file):
         images = []
             confidence = probs[0][pred_class].item()
         return pred_class, confidence
+    def generate_html_result(self, images, max_proj, pred_class, confidence, source, num_images):
+        """Generate HTML result card"""
+        # Create matplotlib figure
         plt.style.use('dark_background')
         fig, axes = plt.subplots(1, 2, figsize=(14, 6))
         fig.patch.set_facecolor('#0a0e27')
         img_base64 = base64.b64encode(buf.read()).decode()
         plt.close()
+        # Generate HTML result card
+        status_class = "status-detected" if pred_class == 1 else "status-not-detected"
+        status_text = "🌟 COMET DETECTED!" if pred_class == 1 else "🌑 No Comet Activity"
+        border_class = "result-detected" if pred_class == 1 else "result-not-detected"
+        confidence_pct = int(confidence * 100)
+        timestamp = datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S UTC')
+        html = f"""
+        <div class="results {border_class}" style="display: block;">
             <div class="result-header">
+                <div class="result-status {status_class}">{status_text}</div>
                 <div class="confidence-bar">
+                    <div class="confidence-fill" style="width: {confidence_pct}%">{confidence_pct}%</div>
                 </div>
             </div>
+            <img class="result-image" src="data:image/png;base64,{img_base64}" alt="Analysis Result">
             <div class="metadata">
                 <div class="metadata-item">
                     <div class="metadata-label">Images Analyzed</div>
+                    <div class="metadata-value">{num_images}</div>
                 </div>
                 <div class="metadata-item">
                     <div class="metadata-label">Data Source</div>
+                    <div class="metadata-value">{source}</div>
                 </div>
                 <div class="metadata-item">
                     <div class="metadata-label">Analysis Time</div>
+                    <div class="metadata-value">{timestamp}</div>
                 </div>
                 <div class="metadata-item">
                     <div class="metadata-label">Model Accuracy</div>
                 </div>
             </div>
         </div>
+        <style>
+        .results {{
+            background: linear-gradient(135deg, rgba(10, 14, 39, 0.95) 0%, rgba(0, 20, 40, 0.9) 100%);
+            border-radius: 15px;
+            padding: 30px;
+            border: 2px solid rgba(0, 217, 255, 0.3);
+            backdrop-filter: blur(10px);
+            animation: slideIn 0.5s ease;
+            margin-top: 20px;
+        }}
+        @keyframes slideIn {{
+            from {{ opacity: 0; transform: translateY(20px); }}
+            to {{ opacity: 1; transform: translateY(0); }}
+        }}
+        .result-detected {{ border-color: #00ff88; box-shadow: 0 0 40px rgba(0, 255, 136, 0.3); }}
+        .result-not-detected {{ border-color: #ffb366; box-shadow: 0 0 40px rgba(255, 179, 102, 0.3); }}
+        .result-header {{ text-align: center; margin-bottom: 30px; }}
+        .result-status {{
+            font-family: 'Orbitron', sans-serif;
+            font-size: 2.5em;
+            font-weight: bold;
+            margin-bottom: 10px;
+        }}
+        .status-detected {{ color: #00ff88; text-shadow: 0 0 20px rgba(0, 255, 136, 0.8); }}
+        .status-not-detected {{ color: #ffb366; text-shadow: 0 0 20px rgba(255, 179, 102, 0.8); }}
+        .confidence-bar {{
+            width: 100%;
+            height: 30px;
+            background: rgba(255, 255, 255, 0.1);
+            border-radius: 15px;
+            overflow: hidden;
+            margin: 20px 0;
+        }}
+        .confidence-fill {{
+            height: 100%;
+            background: linear-gradient(90deg, #00d9ff 0%, #00ff88 100%);
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-weight: bold;
+            color: #000;
+            transition: width 1s ease;
+        }}
+        .result-image {{
+            width: 100%;
+            border-radius: 10px;
+            margin: 20px 0;
+            border: 2px solid rgba(0, 217, 255, 0.3);
+        }}
+        .metadata {{
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 15px;
+            margin-top: 20px;
+        }}
+        .metadata-item {{
+            background: rgba(0, 217, 255, 0.1);
+            padding: 15px;
+            border-radius: 8px;
+            border: 1px solid rgba(0, 217, 255, 0.3);
+        }}
+        .metadata-label {{ color: #00d9ff; font-size: 0.9em; margin-bottom: 5px; }}
+        .metadata-value {{ font-size: 1.2em; font-weight: bold; color: #fff; }}
+        </style>
+        """
+        return html
+    def analyze_uploaded(self, zip_file, progress=gr.Progress()):
+        """Analyze uploaded ZIP with progress tracking"""
+        if zip_file is None:
+            return "<div class='error' style='display:block;'>❌ No file uploaded</div>"
+        try:
+            progress(0.1, desc="Extracting ZIP file...")
+            images = self.load_fits_from_zip(zip_file)
+            if len(images) < 2:
+                return "<div class='error' style='display:block;'>❌ Need at least 2 FITS images</div>"
+            progress(0.4, desc=f"Processing {len(images)} images...")
+            max_proj = self.create_difference_images(images)
+            progress(0.7, desc="Running AI classification...")
+            pred_class, confidence = self.classify_image(max_proj)
+            progress(1.0, desc="Complete!")
+            return self.generate_html_result(
+                images, max_proj, pred_class, confidence,
+                "Uploaded Data", len(images)
+            )
+        except Exception as e:
+            return f"<div class='error' style='display:block;'>❌ Error: {str(e)}</div>"
+    def analyze_soho_live(self, progress=gr.Progress()):
+        """Fetch and analyze live SOHO data with progress"""
+        try:
+            progress(0.2, desc="Fetching SOHO data...")
+            images = self.fetch_soho_recent()
+            progress(0.5, desc="Creating difference images...")
+            max_proj = self.create_difference_images(images)
+            progress(0.8, desc="Running AI classification...")
+            pred_class, confidence = self.classify_image(max_proj)
+            progress(1.0, desc="Complete!")
+            return self.generate_html_result(
+                images, max_proj, pred_class, confidence,
+                "Live SOHO Data (Demo)", len(images)
+            )
+        except Exception as e:
+            return f"<div class='error' style='display:block;'>❌ Error: {str(e)}</div>"
+detector = CometDetectorAPI('best_model.pth')
+# Custom CSS
+custom_css = """
+@import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;700;900&family=Share+Tech+Mono&display=swap');
+* { margin: 0; padding: 0; box-sizing: border-box; }
+body {
+    font-family: 'Share Tech Mono', monospace;
+    background: linear-gradient(135deg, #000814 0%, #001d3d 50%, #000814 100%);
+    color: #fff;
+}
+.gradio-container {
+    max-width: 1400px !important;
+    margin: 0 auto !important;
+}
+.header {
+    text-align: center;
+    padding: 40px 20px;
+    background: linear-gradient(135deg, rgba(10, 14, 39, 0.9) 0%, rgba(0, 20, 40, 0.8) 100%);
+    border-radius: 20px;
+    border: 2px solid rgba(0, 217, 255, 0.3);
+    box-shadow: 0 0 40px rgba(0, 217, 255, 0.2);
+    margin-bottom: 30px;
+    backdrop-filter: blur(10px);
+}
+.title {
+    font-family: 'Orbitron', sans-serif;
+    font-size: 4em;
+    font-weight: 900;
+    background: linear-gradient(135deg, #00d9ff 0%, #00ff88 50%, #ff00ff 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    margin-bottom: 10px;
+    animation: glow 2s ease-in-out infinite alternate;
+}
+@keyframes glow {
+    from { filter: drop-shadow(0 0 5px rgba(0, 217, 255, 0.5)); }
+    to { filter: drop-shadow(0 0 20px rgba(0, 217, 255, 0.8)); }
+}
+.subtitle {
+    font-size: 1.2em;
+    color: #00d9ff;
+    letter-spacing: 3px;
+    margin-bottom: 20px;
+}
+.team {
+    font-size: 1.1em;
+    color: #00ff88;
+    margin-top: 15px;
+}
+.team-member {
+    display: inline-block;
+    padding: 8px 20px;
+    margin: 0 10px;
+    background: linear-gradient(135deg, rgba(0, 255, 136, 0.2) 0%, rgba(0, 217, 255, 0.2) 100%);
+    border: 2px solid #00ff88;
+    border-radius: 25px;
+    font-weight: bold;
+    animation: pulse 2s ease-in-out infinite;
+    box-shadow: 0 0 20px rgba(0, 255, 136, 0.3);
+}
+@keyframes pulse {
+    0%, 100% { transform: scale(1); box-shadow: 0 0 20px rgba(0, 255, 136, 0.3); }
+    50% { transform: scale(1.05); box-shadow: 0 0 30px rgba(0, 255, 136, 0.5); }
+}
+.panel {
+    background: linear-gradient(135deg, rgba(10, 14, 39, 0.95) 0%, rgba(0, 20, 40, 0.9) 100%);
+    border-radius: 15px;
+    padding: 30px;
+    border: 2px solid rgba(0, 217, 255, 0.3);
+    backdrop-filter: blur(10px);
+    margin-bottom: 20px;
+}
+.gr-button-primary {
+    background: linear-gradient(135deg, #00d9ff 0%, #00ff88 100%) !important;
+    border: none !important;
+    color: #000 !important;
+    font-family: 'Orbitron', sans-serif !important;
+    font-weight: bold !important;
+    font-size: 1.1em !important;
+    padding: 15px 40px !important;
+    letter-spacing: 2px !important;
+    transition: all 0.3s ease !important;
+}
+.gr-button-primary:hover {
+    transform: translateY(-3px) !important;
+    box-shadow: 0 10px 30px rgba(0, 217, 255, 0.5) !important;
+}
+.gr-button-secondary {
+    background: linear-gradient(135deg, #ff00ff 0%, #ff0080 100%) !important;
+    border: none !important;
+    color: #fff !important;
+    font-family: 'Orbitron', sans-serif !important;
+    font-weight: bold !important;
+    font-size: 1.1em !important;
+    padding: 15px 40px !important;
+    letter-spacing: 2px !important;
+    transition: all 0.3s ease !important;
+}
+.gr-button-secondary:hover {
+    transform: translateY(-3px) !important;
+    box-shadow: 0 10px 30px rgba(255, 0, 255, 0.5) !important;
+}
+.error {
+    background: rgba(255, 0, 0, 0.1);
+    border: 2px solid rgba(255, 0, 0, 0.5);
+    color: #ff4444;
+    padding: 20px;
+    border-radius: 10px;
+    margin: 20px 0;
+    font-size: 1.1em;
+}
 """
+# Create Gradio Interface
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft(), title="COMET-SEE Mission Control") as demo:
+    gr.HTML("""
+        <div class="header">
+            <div class="title">COMET-SEE</div>
+            <div class="subtitle">COmet Motion Extraction & Tracking – Statistical Exploration Engine</div>
+            <div class="team">
+                <span class="team-member">👩‍🚀 SAMBHAVI</span>
+                <span class="team-member">👩‍🔬 EMILY</span>
+                <span class="team-member">👨‍💻 MOHAMMED</span>
+            </div>
+        </div>
+    """)
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.HTML('<div class="panel"><h2 style="color: #00d9ff; font-family: Orbitron;">📤 Upload FITS Data</h2></div>')
+            file_input = gr.File(
+                label="Upload ZIP of FITS Images",
+                file_types=[".zip"],
+                type="filepath"
+            )
+            upload_btn = gr.Button("🔍 Analyze Upload", variant="primary", size="lg")
+        with gr.Column(scale=1):
+            gr.HTML('<div class="panel"><h2 style="color: #00d9ff; font-family: Orbitron;">📡 Live SOHO Data</h2></div>')
+            gr.HTML("""
+                <div style="text-align: center; padding: 20px;">
+                    <div style="font-size: 3em; margin-bottom: 15px;">🛰️</div>
+                    <p style="color: #aaa; margin-bottom: 20px;">
+                        Fetch recent SOHO/LASCO C3 images<br>and analyze for comet activity
+                    </p>
+                </div>
+            """)
+            fetch_btn = gr.Button("🌐 Fetch Live Data", variant="secondary", size="lg")
+    # Results section
+    result_output = gr.HTML(label="Analysis Results")
+    # Connect buttons
+    upload_btn.click(
+        fn=detector.analyze_uploaded,
+        inputs=[file_input],
+        outputs=[result_output]
+    )
+    fetch_btn.click(
+        fn=detector.analyze_soho_live,
+        outputs=[result_output]
+    )
+    gr.HTML("""
+        <div style="text-align: center; margin-top: 40px; padding: 20px; color: #888;">
+            <p>Model Performance: 97.7% Accuracy • 98% Precision • 99% Recall</p>
+            <p style="margin-top: 10px;">Built with ❤️ by Sambhavi, Emily & Mohammed</p>
+        </div>
+    """)
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)