major server manager overhaul
This commit is contained in:
@@ -0,0 +1,2 @@
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# Add your Google Gemini API key here
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GEMINI_API_KEY=
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@@ -0,0 +1,220 @@
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import os
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import sys
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import glob
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import time
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import json
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import argparse
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import mimetypes
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from pathlib import Path
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from dotenv import load_dotenv
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import google.generativeai as genai
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from google.generativeai.types import HarmCategory, HarmBlockThreshold
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# Load environment variables
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env_path = Path(__file__).parent / "ai_summary.env"
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load_dotenv(dotenv_path=env_path)
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API_KEY = os.getenv("GEMINI_API_KEY")
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if not API_KEY:
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print("Error: GEMINI_API_KEY environment variable not found.")
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print("Please create a .env file with your API key or set it in your environment.")
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sys.exit(1)
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genai.configure(api_key=API_KEY)
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# Configuration
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# Using Gemini 1.5 Flash for speed and cost-efficiency with video
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MODEL_NAME = 'gemini-1.5-flash'
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VIDEO_EXTENSIONS = {'.mp4', '.mov', '.avi', '.mkv', '.webm', '.flv'}
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def setup_model():
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# Adjust safety settings to allow processing of the described content
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# Note: The API may still block content based on its own internal filters.
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safety_settings = {
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HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
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}
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generation_config = {
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"temperature": 0.4,
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"response_mime_type": "application/json",
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}
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model = genai.GenerativeModel(
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model_name=MODEL_NAME,
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safety_settings=safety_settings,
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generation_config=generation_config
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)
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return model
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def upload_video(video_path):
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print(f"Uploading {video_path.name}...")
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try:
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video_file = genai.upload_file(path=video_path)
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print(f"Upload complete: {video_file.name}")
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# Wait for processing
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while video_file.state.name == "PROCESSING":
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print('.', end='', flush=True)
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time.sleep(2)
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video_file = genai.get_file(video_file.name)
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if video_file.state.name == "FAILED":
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print(f"\nProcessing failed for {video_path.name}")
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return None
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print(f"\nVideo is ready.")
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return video_file
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except Exception as e:
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print(f"Error uploading file: {e}")
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return None
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def analyze_video(model, video_file):
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prompt = """
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Analyze this video and provide a summary of the actions occurring in it.
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Return a JSON object with the following fields:
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- \"summary\": A detailed text description of what happens in the video.
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- \"contains_only_blowjobs_no_sex\": Boolean (true/false). Set to true ONLY if the video contains oral sex (blowjobs) but DOES NOT contain penetration sex (vaginal or anal).
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"""
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try:
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response = model.generate_content([video_file, prompt])
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return json.loads(response.text)
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except Exception as e:
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print(f"Error generating content: {e}")
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return None
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def update_html_report(report_path, video_name, summary, flag):
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# Simple append logic for HTML
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# Check if file exists to write header
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file_exists = os.path.exists(report_path)
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with open(report_path, 'a', encoding='utf-8') as f:
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if not file_exists:
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f.write("""
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<!DOCTYPE html>
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<html>
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<head>
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<style>
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table { border-collapse: collapse; width: 100%; }
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th, td { border: 1px solid #ddd; padding: 8px; text-align: left; }
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tr:nth-child(even) { background-color: #f2f2f2; }
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.flagged { color: red; font-weight: bold; }
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</style>
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</head>
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<body>
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<h2>Video Analysis Report</h2>
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<table>
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<tr>
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<th>File Name</th>
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<th>Summary</th>
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<th>Criteria Met (Only BJ, No Sex)</th>
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</tr>
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""")
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row_class = ' class="flagged"' if flag else ""
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f.write(f" <tr>\n <td>{video_name}</td>\n <td>{summary}</td>\n <td{row_class}>{flag}</td>\n </tr>\n")
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def close_html_report(report_path):
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if os.path.exists(report_path):
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with open(report_path, 'a', encoding='utf-8') as f:
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f.write("</table>\n</body>\n</html>")
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def main():
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parser = argparse.ArgumentParser(description="Analyze videos for specific content.")
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parser.add_argument("directory", help="Target directory containing videos")
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args = parser.parse_args()
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target_dir = Path(args.directory)
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if not target_dir.is_dir():
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print(f"Directory not found: {target_dir}")
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sys.exit(1)
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model = setup_model()
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files_to_delete = []
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# List all video files
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video_files = [
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f for f in target_dir.iterdir()
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if f.is_file() and f.suffix.lower() in VIDEO_EXTENSIONS
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]
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print(f"Found {len(video_files)} videos in {target_dir}")
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report_path = target_dir / "analysis_report.html"
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# Remove old report if exists to start fresh? Or append?
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# Let's start fresh for this run
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if report_path.exists():
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os.remove(report_path)
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for video_path in video_files:
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print(f"\nProcessing: {video_path.name}")
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# Check if summary already exists to skip?
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# Requirement implies we run analysis. Let's assume we run on all.
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uploaded_file = upload_video(video_path)
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if not uploaded_file:
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continue
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result = analyze_video(model, uploaded_file)
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# Cleanup remote file to save storage/quota (optional but good practice)
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try:
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genai.delete_file(uploaded_file.name)
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except:
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pass
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if result:
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summary = result.get("summary", "No summary provided.")
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criteria_met = result.get("contains_only_blowjobs_no_sex", False)
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# Save text summary
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txt_path = video_path.with_suffix('.txt')
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with open(txt_path, 'w', encoding='utf-8') as f:
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f.write(summary)
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print(f"Saved summary to {txt_path.name}")
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# Update HTML
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update_html_report(report_path, video_path.name, summary, criteria_met)
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if criteria_met:
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print("--> MATCHES CRITERIA: Only blowjobs, no sex.")
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files_to_delete.append(video_path)
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else:
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print("--> Does not match deletion criteria.")
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else:
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print("Failed to analyze video content.")
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close_html_report(report_path)
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print(f"\nAnalysis complete. Report saved to {report_path}")
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if files_to_delete:
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print("\n" + "="*40)
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print(f"Found {len(files_to_delete)} files matching 'Only Blowjobs, No Sex':")
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for f in files_to_delete:
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print(f"- {f.name}")
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print("="*40)
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confirm = input("\nDo you want to DELETE these files? (yes/no): ").lower()
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if confirm == 'yes':
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for f in files_to_delete:
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try:
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os.remove(f)
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# Also remove the generated text file?
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# Usually better to keep the summary or delete it too.
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# User asked to delete the files (implying videos).
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# I'll delete the video.
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print(f"Deleted: {f.name}")
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except OSError as e:
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print(f"Error deleting {f.name}: {e}")
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else:
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print("Deletion cancelled.")
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else:
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print("\nNo files matched the deletion criteria.")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,202 @@
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import os
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import sys
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import glob
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import time
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import argparse
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from pathlib import Path
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import cv2 # opencv-python
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import ollama
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# Configuration
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# 'moondream' is extremely lightweight (1.6B params).
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# If you want more accuracy at the cost of speed, change this to 'llava' (7B params).
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MODEL_NAME = 'moondream'
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# How many seconds to skip between frames.
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# Higher = Faster processing, but might miss short actions.
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FRAME_INTERVAL_SECONDS = 5
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VIDEO_EXTENSIONS = {'.mp4', '.mov', '.avi', '.mkv', '.webm', '.flv'}
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def analyze_frame(frame_bytes):
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"""Sends a single image frame to the local model for description."""
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try:
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response = ollama.chat(model=MODEL_NAME, messages=[
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{
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'role': 'user',
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'content': 'Describe the sexual activity in this image in detail. Be specific about whether there is oral sex or penetration.',
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'images': [frame_bytes]
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}
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])
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return response['message']['content']
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except Exception as e:
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print(f"Error communicating with Ollama: {e}")
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return ""
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def process_video(video_path):
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"""Extracts frames and aggregates analysis."""
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cap = cv2.VideoCapture(str(video_path))
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if not cap.isOpened():
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print(f"Error opening video: {video_path}")
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return None, False
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fps = cap.get(cv2.CAP_PROP_FPS)
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frame_interval = int(fps * FRAME_INTERVAL_SECONDS)
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frame_count = 0
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descriptions = []
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print(f"Scanning {video_path.name} (taking 1 frame every {FRAME_INTERVAL_SECONDS}s)...")
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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 % frame_interval == 0:
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# Convert frame to bytes for Ollama
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_, buffer = cv2.imencode('.jpg', frame)
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frame_bytes = buffer.tobytes()
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# Analyze frame
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desc = analyze_frame(frame_bytes)
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if desc:
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descriptions.append(desc)
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# print(f" [Frame {frame_count}] {desc[:50]}...") # Uncomment for debug noise
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frame_count += 1
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cap.release()
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if not descriptions:
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return "No frames analyzed.", False
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# Aggregate Logic
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# We join all descriptions and check keywords.
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# This is a naive heuristic because the model doesn't have "memory" of the whole video context,
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# just individual snapshots.
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full_text = " ".join(descriptions).lower()
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has_oral = 'oral' in full_text or 'blowjob' in full_text or 'sucking' in full_text or 'fellatio' in full_text
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has_penetration = 'penetration' in full_text or 'sex' in full_text or 'intercourse' in full_text or 'vaginal' in full_text or 'anal' in full_text or 'fucking' in full_text
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# Refined logic: simple keyword matching can be prone to false positives/negatives with small models.
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# However, for an automated script, this is the baseline.
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summary = f"Analyzed {len(descriptions)} frames.\n\nCombined Observations:\n{full_text}"
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# Criteria: Only Blowjobs (Oral), NO Sex (Penetration)
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# Note: 'sex' is a broad term. Small models might use it generically.
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# You might need to tune 'has_penetration' keywords based on model behavior.
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is_match = has_oral and not has_penetration
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return summary, is_match
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def update_html_report(report_path, video_name, summary, flag):
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file_exists = os.path.exists(report_path)
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with open(report_path, 'a', encoding='utf-8') as f:
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if not file_exists:
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f.write("""
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<!DOCTYPE html>
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<html>
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<head>
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<style>
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table { border-collapse: collapse; width: 100%; }
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th, td { border: 1px solid #ddd; padding: 8px; text-align: left; vertical-align: top;}
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tr:nth-child(even) { background-color: #f2f2f2; }
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.flagged { color: red; font-weight: bold; }
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.summary-text { max-height: 200px; overflow-y: auto; display: block; }
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</style>
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</head>
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<body>
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<h2>Local Video Analysis Report</h2>
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<table>
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<tr>
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<th>File Name</th>
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<th>Summary (Frame Aggregation)</th>
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<th>Criteria Met (Only BJ, No Sex)</th>
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</tr>
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""")
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row_class = ' class="flagged"' if flag else ""
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# Truncate summary for HTML display to avoid massive cells
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display_summary = summary[:1000] + "..." if len(summary) > 1000 else summary
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f.write(f" <tr>\n <td>{video_name}</td>\n <td><div class='summary-text'>{display_summary}</div></td>\n <td{row_class}>{flag}</td>\n </tr>\n")
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def close_html_report(report_path):
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if os.path.exists(report_path):
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with open(report_path, 'a', encoding='utf-8') as f:
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f.write("</table>\n</body>\n</html>")
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def main():
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parser = argparse.ArgumentParser(description="Analyze videos locally using Ollama.")
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parser.add_argument("directory", help="Target directory containing videos")
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args = parser.parse_args()
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target_dir = Path(args.directory)
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if not target_dir.is_dir():
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print(f"Directory not found: {target_dir}")
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sys.exit(1)
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files_to_delete = []
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video_files = [
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f for f in target_dir.iterdir()
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if f.is_file() and f.suffix.lower() in VIDEO_EXTENSIONS
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]
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print(f"Found {len(video_files)} videos in {target_dir}")
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print(f"Using local model: {MODEL_NAME}")
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report_path = target_dir / "local_analysis_report.html"
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if report_path.exists():
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os.remove(report_path)
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for video_path in video_files:
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print(f"\nProcessing: {video_path.name}")
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summary, criteria_met = process_video(video_path)
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if summary:
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# Save text summary
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txt_path = video_path.with_suffix('.txt')
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with open(txt_path, 'w', encoding='utf-8') as f:
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f.write(summary)
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print(f"Saved summary to {txt_path.name}")
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# Update HTML
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update_html_report(report_path, video_path.name, summary, criteria_met)
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if criteria_met:
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print("--> MATCHES CRITERIA: Only blowjobs, no sex.")
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files_to_delete.append(video_path)
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else:
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print("--> Does not match deletion criteria.")
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else:
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print("Skipped (no content analyzing)")
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close_html_report(report_path)
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print(f"\nAnalysis complete. Report saved to {report_path}")
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if files_to_delete:
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print("\n" + "="*40)
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print(f"Found {len(files_to_delete)} files matching 'Only Blowjobs, No Sex':")
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for f in files_to_delete:
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print(f"- {f.name}")
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print("="*40)
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confirm = input("\nDo you want to DELETE these files? (yes/no): ").lower()
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if confirm == 'yes':
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for f in files_to_delete:
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try:
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os.remove(f)
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print(f"Deleted: {f.name}")
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except OSError as e:
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print(f"Error deleting {f.name}: {e}")
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else:
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print("Deletion cancelled.")
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else:
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print("\nNo files matched the deletion criteria.")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,4 @@
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google-generativeai
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python-dotenv
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opencv-python
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ollama
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Reference in New Issue
Block a user