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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 transformers import pipeline, CLIPProcessor, CLIPModel |
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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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news_models = { |
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"mrm8488": pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection"), |
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"google-electra": pipeline("text-classification", model="google/electra-base-discriminator"), |
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"bert-base": pipeline("text-classification", model="bert-base-uncased") |
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} |
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clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") |
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clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") |
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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: 850px; margin: auto; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1); } |
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textarea, select, input[type='file'] { 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; margin-top: 10px; } |
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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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<label for="model">Select Fake News Model:</label> |
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<select name="model" required> |
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<option value="mrm8488">MRM8488 (BERT-Tiny)</option> |
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<option value="google-electra">Google Electra (Base Discriminator)</option> |
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<option value="bert-base">BERT-Base Uncased</option> |
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</select> |
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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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</div> |
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{% endif %} |
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<h1>🖼️ AI vs. Human Image Detection</h1> |
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<form method="POST" action="/detect_image" enctype="multipart/form-data"> |
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<input type="file" name="image" required> |
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<button type="submit">Upload and Detect</button> |
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</form> |
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{% if image_prediction %} |
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<div class="result"> |
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<h2>📷 Image Detection Result:</h2> |
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<p>{{ image_prediction|safe }}</p> |
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<p><strong>Explanation:</strong> The model compares the uploaded image against the text prompts "AI-generated image" and "Human-created image" to determine similarity. Higher similarity to the AI prompt suggests an AI-generated image, and vice versa.</p> |
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</div> |
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{% endif %} |
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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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model_key = request.form.get("model") |
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if not text or model_key not in news_models: |
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return render_template_string(HTML_TEMPLATE, news_prediction="Invalid input or model selection.") |
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result = news_models[model_key](text)[0] |
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label = "REAL" if result['label'].lower() in ["real", "label_1", "neutral"] else "FAKE" |
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confidence = result['score'] * 100 |
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prediction_text = f"News is {label} (Confidence: {confidence:.2f}%)" |
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return render_template_string(HTML_TEMPLATE, news_prediction=prediction_text) |
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@app.route("/detect_image", methods=["POST"]) |
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def detect_image(): |
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if "image" not in request.files: |
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return render_template_string(HTML_TEMPLATE, image_prediction="No image uploaded.") |
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file = request.files["image"] |
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img = Image.open(file).convert("RGB") |
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prompts = ["AI-generated image", "Human-created image"] |
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inputs = clip_processor(text=prompts, images=img, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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outputs = clip_model(**inputs) |
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similarity = outputs.logits_per_image.softmax(dim=1).squeeze().tolist() |
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ai_similarity, human_similarity = similarity |
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prediction = "AI-Generated" if ai_similarity > human_similarity else "Human-Created" |
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prediction_text = ( |
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f"Prediction: <strong>{prediction}</strong><br>" |
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f"AI Similarity: {ai_similarity * 100:.2f}% | Human Similarity: {human_similarity * 100:.2f}%" |
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) |
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return render_template_string(HTML_TEMPLATE, image_prediction=prediction_text) |
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if __name__ == "__main__": |
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app.run(host="0.0.0.0", port=7860) |
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