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import os
from flask import Flask, request, render_template_string
from PIL import Image
import torch
from transformers import pipeline, CLIPProcessor, CLIPModel
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)
# Fake News Detection Models
news_models = {
"mrm8488": pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection"),
"google-electra": pipeline("text-classification", model="google/electra-base-discriminator"),
"bert-base": pipeline("text-classification", model="bert-base-uncased")
}
# Image Detection Model (CLIP-based)
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
# HTML Template with both Fake News and Image Detection
HTML_TEMPLATE = """
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>AI & News 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: 850px; margin: auto; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1); }
textarea, select, 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; margin-top: 10px; }
button:hover { background-color: #45a049; }
.result { background: #e7f3fe; padding: 15px; border-radius: 10px; margin-top: 20px; }
</style>
</head>
<body>
<div class="container">
<h1>📰 Fake News Detection</h1>
<form method="POST" action="/detect">
<textarea name="text" placeholder="Enter news text..." required></textarea>
<label for="model">Select Fake News Model:</label>
<select name="model" required>
<option value="mrm8488">MRM8488 (BERT-Tiny)</option>
<option value="google-electra">Google Electra (Base Discriminator)</option>
<option value="bert-base">BERT-Base Uncased</option>
</select>
<button type="submit">Detect News Authenticity</button>
</form>
{% if news_prediction %}
<div class="result">
<h2>🧠 News Detection Result:</h2>
<p>{{ news_prediction }}</p>
</div>
{% endif %}
<h1>🖼️ AI vs. Human Image Detection</h1>
<form method="POST" action="/detect_image" enctype="multipart/form-data">
<input type="file" name="image" required>
<button type="submit">Upload and Detect</button>
</form>
{% if image_prediction %}
<div class="result">
<h2>📷 Image Detection Result:</h2>
<p>{{ image_prediction|safe }}</p>
<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>
</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")
model_key = request.form.get("model")
if not text or model_key not in news_models:
return render_template_string(HTML_TEMPLATE, news_prediction="Invalid input or model selection.")
result = news_models[model_key](text)[0]
label = "REAL" if result['label'].lower() in ["real", "label_1", "neutral"] else "FAKE"
confidence = result['score'] * 100
prediction_text = f"News is {label} (Confidence: {confidence:.2f}%)"
return render_template_string(HTML_TEMPLATE, news_prediction=prediction_text)
@app.route("/detect_image", methods=["POST"])
def detect_image():
if "image" not in request.files:
return render_template_string(HTML_TEMPLATE, image_prediction="No image uploaded.")
file = request.files["image"]
img = Image.open(file).convert("RGB")
# Compare with AI and Human prompts
prompts = ["AI-generated image", "Human-created image"]
inputs = clip_processor(text=prompts, images=img, return_tensors="pt", padding=True)
with torch.no_grad():
outputs = clip_model(**inputs)
similarity = outputs.logits_per_image.softmax(dim=1).squeeze().tolist()
ai_similarity, human_similarity = similarity
prediction = "AI-Generated" if ai_similarity > human_similarity else "Human-Created"
prediction_text = (
f"Prediction: <strong>{prediction}</strong><br>"
f"AI Similarity: {ai_similarity * 100:.2f}% | Human Similarity: {human_similarity * 100:.2f}%"
)
return render_template_string(HTML_TEMPLATE, image_prediction=prediction_text)
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860)
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