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import os |
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from flask import Flask, request, render_template_string |
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from PIL import Image |
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import torch |
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from torchvision import models, transforms |
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import requests |
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from transformers import pipeline |
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app = Flask(__name__) |
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upload_folder = os.path.join('static', 'uploads') |
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os.makedirs(upload_folder, exist_ok=True) |
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imagenet_class_labels_url = 'https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json' |
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response = requests.get(imagenet_class_labels_url) |
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imagenet_class_labels = response.json() |
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resnet50_model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT) |
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resnet50_model.eval() |
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resnet18_model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT) |
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resnet18_model.eval() |
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news_classifier = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection") |
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transform = transforms.Compose([ |
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transforms.Resize((224, 224)), |
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transforms.ToTensor(), |
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
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]) |
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HTML_TEMPLATE = """ |
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<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<title>AI & News Detection</title> |
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<style> |
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body { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; background-color: #f5f5f5; padding: 20px; } |
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.container { background: white; padding: 30px; border-radius: 12px; max-width: 750px; margin: auto; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1); } |
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h1, h2 { color: #333; } |
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textarea { width: 100%; padding: 12px; margin-top: 10px; border-radius: 8px; border: 1px solid #ccc; } |
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button { background-color: #4CAF50; color: white; border: none; padding: 12px 20px; border-radius: 8px; cursor: pointer; font-size: 16px; } |
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button:hover { background-color: #45a049; } |
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.result { background: #e7f3fe; padding: 15px; border-radius: 10px; margin-top: 20px; } |
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</style> |
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</head> |
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<body> |
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<div class="container"> |
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<h1>📰 Fake News Detection</h1> |
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<form method="POST" action="/detect"> |
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<textarea name="text" placeholder="Enter news text..." required></textarea> |
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<button type="submit">Detect News Authenticity</button> |
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</form> |
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{% if news_prediction %} |
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<div class="result"> |
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<h2>🧠 News Detection Result:</h2> |
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<p>{{ news_prediction }}</p> |
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<p><strong>Interpretation:</strong> This result indicates whether the submitted news text is likely real or fake. Higher confidence suggests stronger model certainty.</p> |
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</div> |
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{% endif %} |
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<div style="margin-top: 30px;"> |
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<h2>🤖 What is ResNet50?</h2> |
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<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> |
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</div> |
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</div> |
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</body> |
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</html> |
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""" |
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@app.route("/", methods=["GET"]) |
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def home(): |
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return render_template_string(HTML_TEMPLATE) |
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@app.route("/detect", methods=["POST"]) |
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def detect(): |
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text = request.form.get("text") |
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if not text: |
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return render_template_string(HTML_TEMPLATE, news_prediction="No text provided.") |
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result = news_classifier(text)[0] |
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label = "REAL" if result['label'] == "LABEL_1" else "FAKE" |
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confidence = result['score'] * 100 |
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return render_template_string( |
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HTML_TEMPLATE, |
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news_prediction=f"News is {label} (Confidence: {confidence:.2f}%)" |
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) |
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if __name__ == "__main__": |
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app.run(host="0.0.0.0", port=7860) |
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