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TruthLens commited on Feb 24

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928e0f8

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1 Parent(s): 723860c

Update app.py

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app.py +58 -20

app.py CHANGED Viewed

@@ -2,7 +2,7 @@ import os
 from flask import Flask, request, render_template_string
 from PIL import Image
 import torch
-from transformers import CLIPProcessor, CLIPModel
 app = Flask(__name__)
@@ -10,28 +10,54 @@ app = Flask(__name__)
 upload_folder = os.path.join('static', 'uploads')
 os.makedirs(upload_folder, exist_ok=True)
-# Load CLIP model and processor
 clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
 clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-# HTML Template with Model Selection and Explanations
 HTML_TEMPLATE = """
 <!DOCTYPE html>
 <html lang="en">
 <head>
     <meta charset="UTF-8">
-    <title>AI vs. Human 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: 850px; margin: auto; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1); }
-        input[type="file"], button { width: 100%; padding: 12px; margin-top: 10px; border-radius: 8px; border: 1px solid #ccc; }
-        button { background-color: #4CAF50; color: white; border: none; font-size: 16px; cursor: pointer; }
         button:hover { background-color: #45a049; }
         .result { background: #e7f3fe; padding: 15px; border-radius: 10px; margin-top: 20px; }
     </style>
 </head>
 <body>
     <div class="container">
         <h1>🖼️ AI vs. Human Image Detection</h1>
         <form method="POST" action="/detect_image" enctype="multipart/form-data">
             <input type="file" name="image" required>
@@ -41,8 +67,8 @@ HTML_TEMPLATE = """
         {% if image_prediction %}
         <div class="result">
             <h2>📷 Image Detection Result:</h2>
-            <p>{{ image_prediction }}</p>
-            <p><strong>Explanation:</strong> The model compares the image to text prompts representing AI-generated and human-created images. The prediction is based on which prompt the image is more similar to.</p>
         </div>
         {% endif %}
     </div>
@@ -54,6 +80,21 @@ HTML_TEMPLATE = """
 def home():
     return render_template_string(HTML_TEMPLATE)
 @app.route("/detect_image", methods=["POST"])
 def detect_image():
     if "image" not in request.files:
@@ -62,26 +103,23 @@ def detect_image():
     file = request.files["image"]
     img = Image.open(file).convert("RGB")
-    # Text prompts for comparison
     prompts = ["AI-generated image", "Human-created image"]
-    # Process image and text inputs
     inputs = clip_processor(text=prompts, images=img, return_tensors="pt", padding=True)
     with torch.no_grad():
         outputs = clip_model(**inputs)
-        logits_per_image = outputs.logits_per_image.squeeze()
-        probs = logits_per_image.softmax(dim=0)
-    ai_score, human_score = probs.tolist()
-    prediction = "AI-Generated" if ai_score > human_score else "Human-Created"
-    result_text = (
-        f"Prediction: {prediction} <br>"
-        f"AI Similarity: {ai_score * 100:.2f}% | Human Similarity: {human_score * 100:.2f}%"
     )
-    return render_template_string(HTML_TEMPLATE, image_prediction=result_text)
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)  # Suitable for public launch

 from flask import Flask, request, render_template_string
 from PIL import Image
 import torch
+from transformers import pipeline, CLIPProcessor, CLIPModel
 app = Flask(__name__)
 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>
         {% 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>
 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 <strong>{label}</strong> (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:
     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)