Add backend files for deployment

app.py

@@ -0,0 +1,408 @@
+from flask import Flask, request, jsonify, render_template, url_for
+from flask_cors import CORS
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import os
+from mtcnn import MTCNN  
+import cv2
+from flask_bcrypt import generate_password_hash, check_password_hash
+from pymongo import MongoClient
+import numpy as np
+from werkzeug.security import generate_password_hash, check_password_hash
+from werkzeug.utils import secure_filename
+import logging
+import matplotlib.pyplot as plt
+import seaborn as sns
+from transformers import AutoImageProcessor, AutoModelForImageClassification  # New imports
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+
+app = Flask(__name__, template_folder="templates", static_folder="static")
+CORS(app)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+UPLOAD_FOLDER = "static/uploads"
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+# ------------------- Model Loading Functions -------------------
+
+def load_model_from_hf(repo_id, filename, num_classes):
+    model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+    model = models.convnext_tiny(weights=None)
+    in_features = model.classifier[2].in_features
+    model.classifier[2] = nn.Linear(in_features, num_classes)
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model.to(device)
+    model.eval()
+    return model
+
+# Load the existing deepfake/cheapfake models
+deepfake_model = load_model_from_hf("faryalnimra/DFDC-detection-model", "DFDC.pth", 2)  
+cheapfake_model = load_model_from_hf("faryalnimra/ORIG-TAMP", "ORIG-TAMP.pth", 1)
+
+# ------------------- New Real/Fake Detector Model -------------------
+# This model determines if the uploaded image is real (label 1) or fake (label 0)
+model_name = "prithivMLmods/Deep-Fake-Detector-Model"
+processor = AutoImageProcessor.from_pretrained(model_name, use_fast=False)
+realfake_detector = AutoModelForImageClassification.from_pretrained(model_name)
+realfake_detector.to(device)
+realfake_detector.eval()
+
+# ------------------- Image Preprocessing -------------------
+
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# ------------------- Face Detector -------------------
+
+face_detector = MTCNN()
+
+def detect_face(image_path):
+    image = cv2.imread(image_path)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    faces = face_detector.detect_faces(image_rgb)
+    face_count = sum(1 for face in faces if face.get("confidence", 0) > 0.90 and face.get("box", [0, 0, 0, 0])[2] > 30)
+    return face_count
+
+# ------------------- API Endpoint: /predict -------------------
+@app.route("/predict", methods=["POST"])
+def predict():
+    if "file" not in request.files:
+        return jsonify({"error": "No file uploaded"}), 400
+    
+    file = request.files["file"]
+    prediction_type = request.form.get("prediction_type", "real_vs_fake")  # default
+
+    filename = os.path.join(UPLOAD_FOLDER, file.filename)
+    file.save(filename)
+
+    try:
+        image = Image.open(filename).convert("RGB")
+        image_tensor = transform(image).unsqueeze(0).to(device)
+    except Exception as e:
+        return jsonify({"error": "Error processing image", "details": str(e)}), 500
+
+    # --------- CASE 1: ONLY Real/Fake Prediction ----------
+    if prediction_type == "real_vs_fake":
+        with torch.no_grad():
+            inputs = processor(images=image, return_tensors="pt").to(device)
+            outputs_realfake = realfake_detector(**inputs)
+            pred_label = torch.argmax(outputs_realfake.logits, dim=1).item()
+
+        if pred_label == 1:
+            return jsonify({
+                "prediction": "Real",
+                "message": "Image is authentic. No further processing.",
+                "image_url": url_for("static", filename=f"uploads/{file.filename}")
+            })
+        else:
+            return jsonify({
+                "prediction": "Fake",
+                "message": "Image is fake, but type (Deepfake/Cheapfake) not determined in this mode.",
+                "image_url": url_for("static", filename=f"uploads/{file.filename}")
+            })
+
+    # --------- CASE 2: Deepfake vs Cheapfake Analysis ----------
+    elif prediction_type == "deepfake_vs_cheapfake":
+        with torch.no_grad():
+            deepfake_probs = torch.softmax(deepfake_model(image_tensor), dim=1)[0]
+            deepfake_confidence_before = deepfake_probs[1].item() * 100  
+            cheapfake_confidence_before = torch.sigmoid(cheapfake_model(image_tensor)).item() * 100  
+
+        face_count = detect_face(filename)
+        face_factor = min(face_count / 2, 1)  
+
+        if deepfake_confidence_before <= cheapfake_confidence_before:
+            adjusted_deepfake_confidence = deepfake_confidence_before * (1 + 0.3 * face_factor)
+            adjusted_cheapfake_confidence = cheapfake_confidence_before * (1 - 0.3 * face_factor)
+        else:
+            adjusted_deepfake_confidence = deepfake_confidence_before
+            adjusted_cheapfake_confidence = cheapfake_confidence_before
+
+        fake_type = "Deepfake" if adjusted_deepfake_confidence > adjusted_cheapfake_confidence else "Cheapfake"
+
+        return jsonify({
+            "prediction": "Fake",
+            "fake_type": fake_type,
+            "deepfake_confidence_before": f"{deepfake_confidence_before:.2f}%",
+            "deepfake_confidence_adjusted": f"{adjusted_deepfake_confidence:.2f}%",
+            "cheapfake_confidence_before": f"{cheapfake_confidence_before:.2f}%",
+            "cheapfake_confidence_adjusted": f"{adjusted_cheapfake_confidence:.2f}%",
+            "faces_detected": face_count,
+            "image_url": url_for("static", filename=f"uploads/{file.filename}")
+        })
+
+    # --------- CASE 3: Invalid prediction_type ---------
+    else:
+        return jsonify({"error": "Invalid prediction_type. Use 'real_vs_fake' or 'deepfake_vs_cheapfake'"}), 400
+
+# ------------------- Heatmap Generator and API -------------------
+
+
+
+# Flask setup
+
+UPLOAD_FOLDER = "static/uploads"
+HEATMAP_FOLDER = "static/heatmaps"
+ALLOWED_EXTENSIONS = {"png", "jpg", "jpeg"}
+
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+os.makedirs(HEATMAP_FOLDER, exist_ok=True)
+
+def allowed_file(filename):
+    return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS
+
+# Load your model
+deepfake_model = load_model_from_hf("faryalnimra/DFDC-detection-model", "DFDC.pth", 2)
+deepfake_model.eval()
+
+# Choose the last Conv2D layer
+target_layer = None
+for name, module in deepfake_model.named_modules():
+    if isinstance(module, torch.nn.Conv2d):
+        target_layer = module
+
+# Grad-CAM class
+class GradCAM:
+    def __init__(self, model, target_layer):
+        self.model = model
+        self.target_layer = target_layer
+        self.gradients = None
+        self.activations = None
+        self._register_hooks()
+
+    def _register_hooks(self):
+        def forward_hook(module, input, output):
+            self.activations = output.detach()
+
+        def backward_hook(module, grad_in, grad_out):
+            self.gradients = grad_out[0].detach()
+
+        self.target_layer.register_forward_hook(forward_hook)
+        self.target_layer.register_backward_hook(backward_hook)
+
+    def generate(self, input_tensor, class_idx=None):
+        self.model.eval()
+        output = self.model(input_tensor)
+
+        if class_idx is None:
+            class_idx = torch.argmax(output, dim=1).item()
+
+        self.model.zero_grad()
+        loss = output[0, class_idx]
+        loss.backward()
+
+        gradients = self.gradients.cpu().numpy()[0]
+        activations = self.activations.cpu().numpy()[0]
+
+        weights = np.mean(gradients, axis=(1, 2))
+        cam = np.zeros(activations.shape[1:], dtype=np.float32)
+
+        for i, w in enumerate(weights):
+            cam += w * activations[i, :, :]
+
+        cam = np.maximum(cam, 0)
+        cam = cv2.resize(cam, (input_tensor.size(3), input_tensor.size(2)))
+        cam = cam - np.min(cam)
+        cam = cam / np.max(cam)
+        return cam, output
+
+# Preprocessing
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+gradcam = GradCAM(deepfake_model, target_layer)
+
+# Generate heatmap and prediction
+def generate_heatmap(original_image_path, heatmap_save_path):
+    img = Image.open(original_image_path).convert("RGB")
+    input_tensor = preprocess(img).unsqueeze(0)
+
+    cam, output = gradcam.generate(input_tensor)
+
+    # Get prediction
+    probabilities = torch.nn.functional.softmax(output, dim=1)[0]
+    class_idx = torch.argmax(probabilities).item()
+    confidence = probabilities[class_idx].item()
+    label = "Fake" if class_idx == 1 else "Real"
+
+    # Generate heatmap
+    heatmap = cv2.applyColorMap(np.uint8(255 * cam), cv2.COLORMAP_JET)
+    heatmap = cv2.GaussianBlur(heatmap, (7, 7), 0)
+    heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
+
+    img_np = np.array(img.resize((224, 224)))
+
+    superimposed_img = heatmap * 0.5 + img_np * 0.5
+    superimposed_img = np.uint8(superimposed_img)
+
+    Image.fromarray(superimposed_img).save(heatmap_save_path)
+
+    return label, confidence
+
+# Flask route
+@app.route("/generate_heatmap", methods=["POST"])
+def generate_heatmap_api():
+    if "file" not in request.files:
+        return jsonify({"error": "No file uploaded"}), 400
+
+    file = request.files["file"]
+
+    if file.filename == "" or not allowed_file(file.filename):
+        return jsonify({"error": "Invalid file type. Allowed types are .png, .jpg, .jpeg"}), 400
+
+    filename = secure_filename(file.filename)
+    original_image_path = os.path.join(UPLOAD_FOLDER, filename)
+
+    try:
+        file.save(original_image_path)
+    except Exception as e:
+        return jsonify({"error": "Failed to save the file"}), 500
+
+    heatmap_filename = f"heatmap_{filename}"
+    heatmap_path = os.path.join(HEATMAP_FOLDER, heatmap_filename)
+
+    label, confidence = generate_heatmap(original_image_path, heatmap_path)
+
+    return jsonify({
+        "original_image_url": url_for("static", filename=f"uploads/{filename}", _external=True),
+        "heatmap_image_url": url_for("static", filename=f"heatmaps/{heatmap_filename}", _external=True),
+        "prediction": label,
+        "confidence": f"{confidence:.2f}"
+    })
+
+# To run:
+# if __name__ == "__main__":
+#     app.run(debug=True)
+
+
+
+
+
+
+
+#MongoDB Atlantis from flask import Flask, request, jsonify
+
+
+# MongoDB connection
+client = MongoClient('mongodb+srv://fakecatcherai:[email protected]/?retryWrites=true&w=majority&appName=Cluster0')
+db = client['fakecatcherDB']
+users_collection = db['users']
+contacts_collection = db['contacts']
+
+def is_valid_password(password):
+    if (len(password) < 8 or
+        not re.search(r'[A-Z]', password) or
+        not re.search(r'[a-z]', password) or
+        not re.search(r'[0-9]', password) or
+        not re.search(r'[!@#$%^&*(),.?":{}|<>]', password)):
+        return False
+    return True
+
+@app.route('/Register', methods=['POST'])
+def register():
+    data = request.get_json()
+    first_name = data.get('firstName')
+    last_name = data.get('lastName')
+    email = data.get('email')
+    password = data.get('password')
+
+    if users_collection.find_one({'email': email}):
+        logging.warning(f"Attempted register with existing email: {email}")
+        return jsonify({'message': 'Email already exists!'}), 400
+
+    # ✅ Password constraints check
+    if not is_valid_password(password):
+        return jsonify({'message': 'Password must be at least 8 characters long and include uppercase, lowercase, number, and special character.'}), 400
+
+    hashed_pw = generate_password_hash(password)
+    users_collection.insert_one({
+        'first_name': first_name,
+        'last_name': last_name,
+        'email': email,
+        'password': hashed_pw
+    })
+
+    logging.info(f"New user registered: {first_name} {last_name}, Email: {email}")
+    return jsonify({'message': 'Registration successful!'}), 201
+
+# 🔵 Login Route
+@app.route('/Login', methods=['POST'])
+def login():
+    data = request.get_json()
+    email = data.get('email')
+    password = data.get('password')
+
+    # Check if the user exists
+    user = users_collection.find_one({'email': email})
+    if not user or not check_password_hash(user['password'], password):
+        logging.warning(f"Failed login attempt for email: {email}")
+        return jsonify({'message': 'Invalid email or password!'}), 401
+
+    logging.info(f"User logged in successfully: {email}")
+    return jsonify({'message': 'Login successful!'}), 200
+@app.route('/ForgotPassword', methods=['POST'])
+def forgot_password():
+    data = request.get_json()
+    email = data.get('email')
+    new_password = data.get('newPassword')
+    confirm_password = data.get('confirmPassword')
+
+    # Check if passwords match
+    if new_password != confirm_password:
+        logging.warning(f"Password reset failed. Passwords do not match for email: {email}")
+        return jsonify({'message': 'Passwords do not match!'}), 400
+
+    # Check if the user exists
+    user = users_collection.find_one({'email': email})
+    if not user:
+        logging.warning(f"Password reset attempt for non-existent email: {email}")
+        return jsonify({'message': 'User not found!'}), 404
+
+    # Hash the new password and update it
+    hashed_pw = generate_password_hash(new_password)
+    users_collection.update_one({'email': email}, {'$set': {'password': hashed_pw}})
+
+    logging.info(f"Password successfully reset for email: {email}")
+    return jsonify({'message': 'Password updated successfully!'}), 200
+
+
+
+
+   
+
+# 🟣 Contact Form Route (React Page: Contact)
+@app.route('/Contact', methods=['POST'])
+def contact():
+    data = request.get_json()
+    email = data.get('email')
+    query = data.get('query')
+    message = data.get('message')
+
+    # Check if all fields are provided
+    if not email or not query or not message:
+        logging.warning(f"Incomplete contact form submission from email: {email}")
+        return jsonify({'message': 'All fields are required!'}), 400
+
+    # Insert the contact data
+    contact_data = {
+        'email': email,
+        'query': query,
+        'message': message
+    }
+    contacts_collection.insert_one(contact_data)
+
+    logging.info(f"Contact form submitted successfully from email: {email}")
+    return jsonify({'message': 'Your message has been sent successfully.'}), 200
+
+if __name__ == '__main__':
+    app.run(debug=True)