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import os
import subprocess
import importlib.util
import gradio as gr
import logging
# Clone the GitHub repository containing the backend
def clone_repo():
"""Clone the GitHub repository containing the backend."""
repo_url = "https://github.com/NeeravSood/AllMark-MVP.git" # Update if necessary
repo_path = "./repository"
# Retrieve the GitHub Personal Access Token (GITHUB_PAT) from environment variables
github_pat = os.getenv("GITHUB_PAT")
if not github_pat:
raise RuntimeError("GitHub Personal Access Token (GITHUB_PAT) not found in environment variables.")
# Modify the repository URL to include the token for authentication
authenticated_repo_url = f"https://{github_pat}@github.com/NeeravSood/AllMark-MVP.git"
if os.path.exists(repo_path):
print("Repository already cloned.")
else:
try:
# Clone the repository using the authenticated URL
subprocess.run(
["git", "clone", authenticated_repo_url, repo_path],
check=True,
text=True,
capture_output=True
)
print("Repository cloned successfully.")
except subprocess.CalledProcessError as e:
print("Output:", e.stdout)
print("Error:", e.stderr)
raise RuntimeError(f"Failed to clone repository: {e.stderr}")
def import_backend_script(script_name):
"""Dynamically import the backend script."""
try:
script_path = os.path.join("./repository", script_name)
if not os.path.exists(script_path):
raise FileNotFoundError(f"Script {script_name} not found in the repository.")
spec = importlib.util.spec_from_file_location("backend_module", script_path)
backend_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(backend_module)
return backend_module
except Exception as e:
logging.error(f"Error importing backend script: {str(e)}")
raise RuntimeError(f"Failed to import backend script: {str(e)}")
# Clone the repository and import the backend module
clone_repo()
backend = import_backend_script("app.py") # Import app.py from the cloned repository
# Initialize the analyzer instance from the imported module
analyzer = backend.DeepfakeAnalyzer() # Use the imported module's class or function
# Define the Gradio function to analyze the video
def analyze_video(video_file):
results = analyzer.analyze_media(video_file)
combined_probability = results['combined_assessment']
audio_analysis = results["audio_analysis"]
video_probability = results['video_analysis']['probability']
frame_count = len(results['video_analysis']['frame_results'])
# Format the output for display
output = {
"Audio Analysis": audio_analysis,
"Deepfake Probability (Combined Assessment)": f"{combined_probability:.2f}%",
"Video Analysis": {
"Deepfake Probability": f"{video_probability:.4f}",
"Frames Analyzed": frame_count,
"Frame Analysis Summary": [
{
"Frame Number": frame_result["frame_number"],
"Noise Level": frame_result["noise"],
"Edge Density": frame_result["edge_density"],
"Color Consistency": frame_result["color_consistency"],
"Temporal Difference": frame_result["temporal_difference"],
"Probability": frame_result["probability"]
}
for frame_result in results['video_analysis']['frame_results']
]
}
}
return output
# Define the Gradio interface
interface = gr.Interface(
fn=analyze_video,
inputs=gr.Video(label="Upload Video"),
outputs="json",
title="Deepfake Analyzer",
description="Upload a video to analyze for deepfake content."
)
# Launch Gradio app
if __name__ == "__main__":
interface.launch()
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