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shanti commited on Feb 22

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c6e7c13

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Create app.py

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+import os
+from flask import Flask, request, render_template_string
+from PIL import Image
+import torch
+from torchvision import models, transforms
+import requests
+from pyngrok import ngrok
+app = Flask(__name__)
+# Create the 'static/uploads' folder if it doesn't exist
+upload_folder = os.path.join('static', 'uploads')
+os.makedirs(upload_folder, exist_ok=True)
+# Download ImageNet class labels
+imagenet_class_labels_url = 'https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json'
+response = requests.get(imagenet_class_labels_url)
+imagenet_class_labels = response.json()
+# Create Ngrok tunnel
+public_url = ngrok.connect(5000)
+print(f"🔗 Public URL: {public_url}")
+# Load pre-trained ResNet50 for object classification
+resnet50_model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
+resnet50_model.eval()
+# Load ResNet18 for AI vs. Human detection (Use custom-trained weights if available)
+resnet18_model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
+resnet18_model.eval()
+# Image transformation pipeline
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+# HTML Template with improved UI and interpretation
+HTML_TEMPLATE = """
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>🌐 AI & Image Detection</title>
+    <style>
+        body { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; background-color: #f5f5f5; padding: 20px; }
+        .container { background: white; padding: 30px; border-radius: 12px; max-width: 750px; margin: auto; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1); }
+        h1, h2 { color: #333; }
+        textarea, input[type="file"] { width: 100%; padding: 12px; margin-top: 10px; border-radius: 8px; border: 1px solid #ccc; }
+        button { background-color: #4CAF50; color: white; border: none; padding: 12px 20px; border-radius: 8px; cursor: pointer; font-size: 16px; }
+        button:hover { background-color: #45a049; }
+        .result { background: #e7f3fe; padding: 15px; border-radius: 10px; margin-top: 20px; }
+        ul { text-align: left; }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>📰 Fake News & Image Detection</h1>
+        <form method="POST" action="/detect">
+            <textarea name="text" placeholder="Enter news text..." required></textarea>
+            <button type="submit">Detect News Authenticity</button>
+        </form>
+        <h1>🖼️ Upload Image for Detection</h1>
+        <form method="POST" action="/detect_image" enctype="multipart/form-data">
+            <input type="file" name="image" required>
+            <button type="submit">Upload and Analyze</button>
+        </form>
+        <div style="margin-top: 30px;">
+            <h2>🤖 What is ResNet50?</h2>
+            <p>ResNet50 is a 50-layer deep convolutional neural network designed for image classification tasks. It can recognize thousands of objects from the ImageNet dataset.</p>
+        </div>
+        {% if ai_prediction %}
+        <div class="result">
+            <h2>🧠 AI vs. Human Detection Result:</h2>
+            <p>{{ ai_prediction }}</p>
+            <p><strong>Interpretation:</strong> This result indicates whether the uploaded image was likely created by AI or a human. Higher confidence suggests stronger model certainty.</p>
+        </div>
+        {% endif %}
+        {% if classification_results %}
+        <div class="result">
+            <h2>📦 Object Classification Results (ResNet50):</h2>
+            <ul>
+                {% for result in classification_results %}
+                <li>• {{ result.label }} ({{ (result.score * 100) | round(2) }}%) - Detected object category.</li>
+                {% endfor %}
+            </ul>
+            <p><strong>Interpretation:</strong> The model predicts the most probable object categories in the uploaded image along with confidence scores. Higher percentages indicate stronger matches.</p>
+        </div>
+        {% endif %}
+    </div>
+</body>
+</html>
+"""
+@app.route("/", methods=["GET"])
+def home():
+    return render_template_string(HTML_TEMPLATE)
+@app.route("/detect", methods=["POST"])
+def detect():
+    text = request.form.get("text")
+    final_label = "REAL" if "trusted" in text.lower() else "FAKE"  # Placeholder logic
+    return render_template_string(HTML_TEMPLATE, ai_prediction=f"News is {final_label}.", classification_results=None)
+@app.route("/detect_image", methods=["POST"])
+def detect_image():
+    if "image" not in request.files:
+        return "No image uploaded.", 400
+    file = request.files["image"]
+    img_path = os.path.join(upload_folder, file.filename)
+    file.save(img_path)
+    img = Image.open(img_path).convert("RGB")
+    img_tensor = transform(img).unsqueeze(0)
+    # AI vs. Human detection
+    with torch.no_grad():
+        ai_output = resnet18_model(img_tensor)
+        ai_confidence = torch.softmax(ai_output, dim=1).max().item()
+        ai_label = "AI-Generated" if ai_confidence > 0.55 else "Human-Created"
+    # Object classification with ResNet50
+    with torch.no_grad():
+        outputs = resnet50_model(img_tensor)
+        probs = torch.softmax(outputs, dim=1)[0]
+        top5_probs, top5_indices = torch.topk(probs, 5)
+        classification_results = [
+            {"label": imagenet_class_labels[idx], "score": prob.item()} for idx, prob in zip(top5_indices, top5_probs)
+        ]
+    return render_template_string(
+        HTML_TEMPLATE,
+        ai_prediction=f"{ai_label} (Confidence: {(ai_confidence * 100):.2f}%)",
+        classification_results=classification_results
+    )
+if __name__ == "__main__":
+    app.run(port=5000)