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Runtime error
Update app.py
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app.py
CHANGED
@@ -7,6 +7,11 @@ from moviepy.editor import VideoFileClip
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import json
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import spaces
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import torch
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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@@ -16,17 +21,21 @@ torch.backends.cudnn.benchmark = False
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torch.backends.cuda.matmul.allow_tf32 = False
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torch.backends.cudnn.allow_tf32 = False
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def truncate_video(video_file):
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"""Truncates video to 15 seconds and saves it as a temporary file."""
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clip = VideoFileClip(video_file)
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truncated_clip = clip.subclip(0, min(15, clip.duration))
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truncated_video_file = "temp_truncated_video.mp4"
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truncated_clip.write_videofile(truncated_video_file, codec="libx264", audio_codec="aac")
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return truncated_video_file
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def clone_repo():
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"""Clone the GitHub repository containing the backend."""
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repo_url = "https://github.com/NeeravSood/AllMark-MVP.git"
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repo_path = "./repository"
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github_pat = os.getenv("GITHUB_PAT")
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@@ -50,6 +59,7 @@ def clone_repo():
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print("Error:", e.stderr)
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raise RuntimeError(f"Failed to clone repository: {e.stderr}")
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def import_backend_script(script_name):
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"""Dynamically import the backend script."""
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try:
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@@ -65,24 +75,41 @@ def import_backend_script(script_name):
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logging.error(f"Error importing backend script: {str(e)}")
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raise RuntimeError(f"Failed to import backend script: {str(e)}")
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clone_repo()
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backend = import_backend_script("app.py")
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analyzer = backend.DeepfakeAnalyzer()
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@spaces.GPU(duration=1000)
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def analyze_video(video_file):
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try:
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truncated_video = truncate_video(video_file)
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results = analyzer.analyze_media(truncated_video)
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#
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combined_assessment = results.get('combined_assessment', 0)
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if isinstance(combined_assessment, str) and combined_assessment.lower() == "deepfake":
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analysis_result = "a deepfake"
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else:
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combined_assessment = int(combined_assessment) if str(combined_assessment).isdigit() else 0
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analysis_result = "genuine/original" if combined_assessment < 50 else "a deepfake"
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output = {
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"message": f"According to our analysis, the video you uploaded appears to be {analysis_result}. "
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f"{len(results['video_analysis']['frame_results'])} frames were analyzed in total."
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@@ -93,14 +120,41 @@ def analyze_video(video_file):
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logging.error(f"Error during analysis: {e}")
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return {"error": "An error occurred during video analysis. Please check your input and try again."}
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interface
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fn=analyze_video,
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inputs=gr.Video(label="Upload Video"),
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outputs="json",
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title="AllMark - Deepfake Analyzer",
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description="Upload a video to analyze. N.B. - Only mp4 files. Processing time 1-10 minutes. For any false negatives, please contact the publisher for verification. Incognito Mode Recommended"
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)
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if __name__ == "__main__":
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import json
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import spaces
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import torch
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import random
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import string
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# Set up logging
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logging.basicConfig(level=logging.ERROR)
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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torch.backends.cuda.matmul.allow_tf32 = False
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torch.backends.cudnn.allow_tf32 = False
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# Function to truncate video
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def truncate_video(video_file):
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"""Truncates video to 15 seconds and saves it as a temporary file."""
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clip = VideoFileClip(video_file)
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truncated_clip = clip.subclip(0, min(15, clip.duration))
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truncated_video_file = "temp_truncated_video.mp4"
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truncated_clip.write_videofile(truncated_video_file, codec="libx264", audio_codec="aac")
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clip.close()
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truncated_clip.close()
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return truncated_video_file
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# Clone repository
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def clone_repo():
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"""Clone the GitHub repository containing the backend."""
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repo_url = "https://github.com/NeeravSood/AllMark-MVP.git"
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repo_path = "./repository"
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github_pat = os.getenv("GITHUB_PAT")
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print("Error:", e.stderr)
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raise RuntimeError(f"Failed to clone repository: {e.stderr}")
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# Import backend script
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def import_backend_script(script_name):
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"""Dynamically import the backend script."""
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try:
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logging.error(f"Error importing backend script: {str(e)}")
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raise RuntimeError(f"Failed to import backend script: {str(e)}")
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# Clone and import repository
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clone_repo()
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backend = import_backend_script("app.py")
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analyzer = backend.DeepfakeAnalyzer()
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# Generate a random CAPTCHA challenge
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def generate_captcha():
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return ''.join(random.choices(string.ascii_uppercase + string.digits, k=5))
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# Initial CAPTCHA
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captcha_solution = generate_captcha()
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def verify_captcha(user_input):
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global captcha_solution
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if user_input.strip() == captcha_solution:
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return "Captcha verified. You can now upload a video.", True, captcha_solution
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else:
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captcha_solution = generate_captcha() # Reset CAPTCHA on failure
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return f"Incorrect CAPTCHA. Please try again.", False, captcha_solution
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@spaces.GPU(duration=1000)
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def analyze_video(video_file):
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try:
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# Video truncation and analysis
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truncated_video = truncate_video(video_file)
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results = analyzer.analyze_media(truncated_video)
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# Process combined assessment
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combined_assessment = results.get('combined_assessment', 0)
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if isinstance(combined_assessment, str) and combined_assessment.lower() == "deepfake":
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analysis_result = "a deepfake"
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else:
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combined_assessment = int(combined_assessment) if str(combined_assessment).isdigit() else 0
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analysis_result = "genuine/original" if combined_assessment < 50 else "a deepfake"
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output = {
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"message": f"According to our analysis, the video you uploaded appears to be {analysis_result}. "
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f"{len(results['video_analysis']['frame_results'])} frames were analyzed in total."
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logging.error(f"Error during analysis: {e}")
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return {"error": "An error occurred during video analysis. Please check your input and try again."}
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# Interface with CAPTCHA verification
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def main_interface():
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with gr.Blocks() as interface:
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gr.Markdown("# AllMark - Deepfake Analyzer")
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gr.Markdown("Please solve the CAPTCHA to proceed with video analysis.")
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# Display CAPTCHA and input box for user response
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captcha_text = gr.Textbox(label="CAPTCHA", value=captcha_solution, interactive=False)
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captcha_input = gr.Textbox(label="Enter CAPTCHA Here")
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captcha_output = gr.Textbox(label="CAPTCHA Status", interactive=False)
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# Verify CAPTCHA button
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verify_button = gr.Button("Verify CAPTCHA")
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# Video analysis components (hidden initially)
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with gr.Column(visible=False) as analysis_components:
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video_input = gr.Video(label="Upload Video")
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analysis_output = gr.JSON(label="Analysis Result")
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# Submit button for video analysis
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analyze_button = gr.Button("Analyze Video")
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analyze_button.click(analyze_video, inputs=video_input, outputs=analysis_output)
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# Handle CAPTCHA verification
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def verify_captcha_click(input_text):
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message, success, new_captcha = verify_captcha(input_text)
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if success:
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analysis_components.visible = True
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return message, new_captcha
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# Update both CAPTCHA message and CAPTCHA text dynamically
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verify_button.click(verify_captcha_click, inputs=captcha_input, outputs=[captcha_output, captcha_text])
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return interface
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# Launch interface
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if __name__ == "__main__":
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main_interface().launch()
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