Update app.py
Browse files
app.py
CHANGED
@@ -316,9 +316,71 @@ if uploaded_image is not None:
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st.error(f"⚠️ Result: This image is a Deepfake. (Confidence: {result['score']:.2f})")
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# ---- Deepfake Video Detection Section ----
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st.subheader("🎥 Deepfake Video Detection")
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uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"])
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def detect_deepfake_video(video_path):
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cap = cv2.VideoCapture(video_path)
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@@ -350,15 +412,25 @@ def detect_deepfake_video(video_path):
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return {"label": final_label, "score": confidence}
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if uploaded_video is not None:
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st.write("🔍 Processing... Please wait.")
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result = detect_deepfake_video(
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if result["label"] == "FAKE":
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st.error(f"⚠️ Deepfake Detected! This video appears to be FAKE. (Confidence: {result['score']:.2f})")
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st.error(f"⚠️ Result: This image is a Deepfake. (Confidence: {result['score']:.2f})")
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# # ---- Deepfake Video Detection Section ----
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# st.subheader("🎥 Deepfake Video Detection")
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# uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"])
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# def detect_deepfake_video(video_path):
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# cap = cv2.VideoCapture(video_path)
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# frame_scores = []
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# frame_count = 0
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# while cap.isOpened():
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# ret, frame = cap.read()
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# if not ret:
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# break
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# if frame_count % 10 == 0: # ہر 10ویں فریم کا تجزیہ کریں
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# frame_path = "temp_frame.jpg"
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# cv2.imwrite(frame_path, frame)
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# result = detect_deepfake_image(frame_path)
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# frame_scores.append(result["score"])
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# os.remove(frame_path)
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# frame_count += 1
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# cap.release()
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# if not frame_scores:
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# return {"label": "UNKNOWN", "score": 0.0} # اگر کوئی فریم پراسیس نہ ہو سکے
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# avg_score = np.mean(frame_scores)
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# confidence = round(float(avg_score), 2)
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# final_label = "FAKE" if avg_score > 0.5 else "REAL"
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# return {"label": final_label, "score": confidence}
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# if uploaded_video is not None:
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# st.video(uploaded_video)
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# temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
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# with open(temp_file.name, "wb") as f:
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# f.write(uploaded_video.read())
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# if st.button("Analyze Video"):
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# st.write("🔍 Processing... Please wait.")
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# result = detect_deepfake_video(temp_file.name)
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# if result["label"] == "FAKE":
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# st.error(f"⚠️ Deepfake Detected! This video appears to be FAKE. (Confidence: {result['score']:.2f})")
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# elif result["label"] == "REAL":
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# st.success(f"✅ This video appears to be REAL. (Confidence: {1 - result['score']:.2f})")
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# else:
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# st.warning("⚠️ Unable to analyze the video. Please try a different file.")
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# ---- Deepfake Video Detection Section ----
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st.subheader("🎥 Deepfake Video Detection")
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uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"])
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video_url = st.text_input("Or enter a video URL")
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def download_video(url):
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temp_video = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
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response = requests.get(url, stream=True)
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if response.status_code == 200:
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with open(temp_video.name, "wb") as f:
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for chunk in response.iter_content(chunk_size=8192):
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f.write(chunk)
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return temp_video.name
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return None
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def detect_deepfake_video(video_path):
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cap = cv2.VideoCapture(video_path)
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return {"label": final_label, "score": confidence}
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if uploaded_video is not None or video_url:
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if uploaded_video is not None:
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st.video(uploaded_video)
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temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
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with open(temp_file.name, "wb") as f:
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f.write(uploaded_video.read())
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video_path = temp_file.name
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elif video_url:
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st.write("📥 Downloading video from URL...")
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video_path = download_video(video_url)
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if video_path:
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st.video(video_path)
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else:
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st.error("⚠️ Failed to download video. Please check the URL and try again.")
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video_path = None
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if video_path and st.button("Analyze Video"):
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st.write("🔍 Processing... Please wait.")
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result = detect_deepfake_video(video_path)
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if result["label"] == "FAKE":
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st.error(f"⚠️ Deepfake Detected! This video appears to be FAKE. (Confidence: {result['score']:.2f})")
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