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app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from tempfile import NamedTemporaryFile
+from tensorflow.lite.python.interpreter import Interpreter
+from PIL import Image
+from datetime import datetime
+import os
+
+# Load the TFLite model
+interpreter = Interpreter(model_path="deepfake_detection_model.tflite")
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+# Function to preprocess video frames
+def preprocess_frames(frames):
+    preprocessed_frames = []
+    for frame in frames:
+        frame = cv2.resize(frame, (224, 224))
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        preprocessed_frames.append(frame)
+    return np.array(preprocessed_frames)
+
+# Function to extract frames from video
+def extract_frames(video_bytes, num_frames=10):
+    frames = []
+    with NamedTemporaryFile(delete=False, suffix='.mp4') as tmp_file:
+        tmp_file.write(video_bytes)
+        tmp_file_path = tmp_file.name
+    
+    cap = cv2.VideoCapture(tmp_file_path)
+    while len(frames) < num_frames:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frames.append(frame)
+    cap.release()
+    
+    # Delete temporary file
+    os.unlink(tmp_file_path)
+    
+    return frames
+
+# Function to predict if the video is real or deepfake
+def predict_video(uploaded_file, status_log):
+    video_bytes = uploaded_file.read()  # Read byte data from file object
+    frames = extract_frames(video_bytes, num_frames=10)
+    preprocessed_frames = preprocess_frames(frames)
+    preprocessed_frames = preprocessed_frames.reshape((1, 10, 224, 224, 3))
+
+    # Run inference
+    interpreter.set_tensor(input_details[0]['index'], preprocessed_frames.astype('float32'))
+    interpreter.invoke()
+    prediction = interpreter.get_tensor(output_details[0]['index'])
+
+    accuracy = prediction[0][0] if prediction[0][0] > 0.5 else 1 - prediction[0][0]
+    result = "Deepfake" if prediction[0][0] > 0.5 else "Real"
+
+    status_log.text(f"[{timestamp}] - The video is {result} with {accuracy * 100:.2f}% accuracy.\n")
+
+    return result, accuracy
+
+def main():
+    st.title("Deepfake Detection App")
+
+    # Section 1: Title and Navigation Bar
+    st.sidebar.title("Navigation")
+    page = st.sidebar.radio("Go to:", ("Home", "About"))
+
+    # Section 2: Video and Model Detection
+    if page == "Home":
+        st.header("Video Player and Model Detection")
+        uploaded_file = st.file_uploader("Upload a video file:", type=["mp4"])
+
+        # Logs Section
+        st.header("Logs:")
+        status_log = st.empty()
+
+        if uploaded_file is not None:
+            status_log.text(f"[{timestamp}] - Video uploaded successfully.\n")
+            st.video(uploaded_file)
+
+            if st.button("Detect"):
+                status_log.text(f"[{timestamp}] - Detecting video...\n")
+                result, accuracy = predict_video(uploaded_file, status_log)
+                
+                # Table displaying model name, live accuracy, and status
+                st.subheader("Model Detection Status")
+                data = {"Model Name": ["Resnet+LSTM"], "Accuracy": [f"{accuracy * 100:.2f}%"], "Status": [result]}
+                st.table(data)
+
+    # Section 3: Log Section
+    elif page == "About":
+        st.header("Log Section")
+        st.write("System logs will be displayed here.")
+
+if __name__ == "__main__":
+    main()

deepfake_detection_model.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:efc20630d33891ee1a3dff4085382bcd511ff073def94c67a5a25da116e1536e
+size 26305120