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app.py

@@ -1,51 +1,76 @@
-
-
-from tensorflow.keras.models import load_model
-from PIL import Image
-import numpy as np
-import gradio as gr
-
-# Loading the trained model
-try:
-    model = load_model('/model.h5')  # Replacing with the path to your saved model
-except Exception as e:
-    print("Error loading the model:", e)
-
-def detect_image(input_image):
-    try:
-        # Function to detect image
-        img = Image.fromarray(input_image).resize((256, 256))  # Resize image
-        img_array = np.array(img) / 255.0  # Normalize pixel values
-        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-
-        prediction = model.predict(img_array)[0][0]
-        probability_real = prediction * 100  # Convert prediction to percentage
-        probability_ai = (1 - prediction) * 100
-
-        # Determine the final output
-        if probability_real > probability_ai:
-            result = 'Input Image is Real'
-            confidence = probability_real
-        else:
-            result = 'Input Image is AI Generated'
-            confidence = probability_ai
-
-        return result, confidence
-    except Exception as e:
-        print("Error detecting image:", e)
-        return "Error detecting image", 0
-
-# Define input and output components for Gradio
-input_image = gr.Image()
-output_text = gr.Textbox(label="Result")
-output_confidence = gr.Textbox(label="Confidence (%)")
-
-# Create Gradio interface
-gr.Interface(
-    fn=detect_image,
-    inputs=input_image,
-    outputs=[output_text, output_confidence],
-    title="Deepfake Detection",
-    description="Upload an image to detect if it's real or AI generated."
-).launch(share=True)
-
+from flask import Flask, request, jsonify, render_template, send_from_directory
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+import os
+
+app = Flask(__name__)
+
+# Loading the trained model
+try:
+    model = load_model('model.h5')  # Replacing with the path to your saved model
+except Exception as e:
+    print("Error loading the model:", e)
+
+def detect_image(image_path):
+    try:
+        # Function to detect image
+        img = Image.open(image_path).resize((256, 256))  # Resizing image
+        img_array = np.array(img) / 255.0  # Normalizing pixel values
+        img_array = np.expand_dims(img_array, axis=0)  # Adding batch dimension
+
+        prediction = model.predict(img_array)[0][0]
+        probability_real = prediction * 100  # Converting prediction to percentage
+        probability_ai = (1 - prediction) * 100
+
+        # Determine the final output
+        if probability_real > probability_ai:
+            result = 'Input Image is Real'
+            confidence = probability_real
+        else:
+            result = 'Input Image is AI Generated'
+            confidence = probability_ai
+
+        return result, confidence
+    except Exception as e:
+        print("Error detecting image:", e)
+        return "Error detecting image", 0
+
+@app.route('/')
+def index():
+    return render_template('home.html')
+
+@app.route('/detect', methods=['POST'])
+def detect():
+    try:
+        if 'file' not in request.files:
+            return jsonify({'error': 'No file provided'})
+
+        file = request.files['file']
+        if file.filename.split('.')[-1].lower() not in ['jpg', 'jpeg', 'png']:
+            return jsonify({'error': 'Unsupported file type. Please provide an image in JPG, JPEG, or PNG format.'})
+
+        file_path = 'DetectionImage/' + file.filename  # Specifying the directory where images will be saved
+        file.save(file_path)
+
+        result, confidence = detect_image(file_path)
+
+        response = {
+            'result': result,
+            'confidence': confidence,
+            'image_path': file_path
+        }
+
+        return jsonify(response)
+    except Exception as e:
+        print("Error processing request:", e)
+        return jsonify({'error': 'Error processing request'})
+
+@app.route('/DetectionImage/<path:filename>')
+def serve_image(filename):
+    return send_from_directory('DetectionImage', filename)
+
+if __name__ == '__main__':
+    if not os.path.exists('DetectionImage'):
+        os.makedirs('DetectionImage')
+    app.run(debug=True)