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TruthLens commited on Feb 22

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7633b69

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1 Parent(s): 1147a5e

Delete 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 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)
-# Load Fake News Detection Pipelines
-news_models = {
-    "nlptown": pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")
-}
-# Load Image Models for AI vs. Human Detection
-clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
-clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
-ai_image_models = {
-    "hila-chefer": CLIPModel.from_pretrained("hila-chefer/ai-generated-image-detector"),
-    "openai": clip_model
-}
-# 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 Model Selection
-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: 800px; margin: auto; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1); }
-        textarea, select { 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="anishathalye">Anishathalye</option>
-                <option value="liam168">Liam168</option>
-                <option value="joeddav">Joeddav (Multilingual)</option>
-                <option value="nlptown">NLPTown (Sentiment-Based)</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>
-            <label for="image_model">Select Image Detection Model:</label>
-            <select name="image_model" required>
-                <option value="osanseviero">Osanseviero (CLIP)</option>
-                <option value="hila-chefer">Hila-Chefer (AI Detector)</option>
-                <option value="openai">OpenAI (CLIP Large)</option>
-            </select>
-            <button type="submit">Upload and Detect</button>
-        </form>
-        {% if image_prediction %}
-        <div class="result">
-            <h2>📷 Image Detection Result:</h2>
-            <p>{{ image_prediction }}</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'] == "LABEL_1" else "FAKE"
-    confidence = result['score'] * 100
-    return render_template_string(
-        HTML_TEMPLATE,
-        news_prediction=f"News is {label} (Confidence: {confidence:.2f}%)"
-    )
-@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"]
-    model_key = request.form.get("image_model")
-    if not model_key or model_key not in ai_image_models:
-        return render_template_string(HTML_TEMPLATE, image_prediction="Invalid model selection.")
-    img = Image.open(file).convert("RGB")
-    inputs = clip_processor(images=img, return_tensors="pt")
-    with torch.no_grad():
-        image_features = ai_image_models[model_key].get_image_features(**inputs)
-    prediction = "AI-Generated" if torch.mean(image_features).item() > 0 else "Human-Created"
-    return render_template_string(
-        HTML_TEMPLATE,
-        image_prediction=f"Prediction: {prediction}"
-    )
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)  # Suitable for Hugging Face Spaces