Update vision_analyzer.py

vision_analyzer.py

@@ -1,748 +1,267 @@
 import os
+import time
 import json
-import requests
 import subprocess
-import shutil
-import time
-import re
 import threading
-from typing import Dict, List, Set, Optional
-from huggingface_hub import HfApi, list_repo_files
-from fastapi import FastAPI, File, UploadFile, Form
-from fastapi.responses import JSONResponse
-from pathlib import Path
-import smtplib
-from email.message import EmailMessage
-import tempfile
-import rarfile
-import zipfile
-import cv2
-import numpy as np
-from PIL import Image
-import torch
-from transformers import AutoProcessor, AutoModelForCausalLM
-
-# Initialize FastAPI
-app = FastAPI()
-
-# ==== CONFIGURATION ====
-HF_TOKEN = os.getenv("HF_TOKEN", "")
-SOURCE_REPO_ID = os.getenv("SOURCE_REPO", "Fred808/BG1")
-
-# Path Configuration
-DOWNLOAD_FOLDER = "downloads"
-EXTRACT_FOLDER = "extracted"
-FRAMES_OUTPUT_FOLDER = "extracted_frames"
-ANALYSIS_OUTPUT_FOLDER = "analysis_results"
-
-os.makedirs(DOWNLOAD_FOLDER, exist_ok=True)
-os.makedirs(EXTRACT_FOLDER, exist_ok=True)
-os.makedirs(FRAMES_OUTPUT_FOLDER, exist_ok=True)
-os.makedirs(ANALYSIS_OUTPUT_FOLDER, exist_ok=True)
-
-# State Files
-DOWNLOAD_STATE_FILE = "download_progress.json"
-PROCESS_STATE_FILE = "process_progress.json"
-FAILED_FILES_LOG = "failed_files.log"
-
-# Processing Parameters
-CHUNK_SIZE = 1
-PROCESSING_DELAY = 2
-MAX_RETRIES = 3
-MIN_FREE_SPACE_GB = 2  # Minimum free space in GB before processing
+from datetime import datetime
 
-# Frame Extraction Parameters
-DEFAULT_FPS = 0.1  # Default frames per second for extraction
-
-# Initialize HF API
-hf_api = HfApi(token=HF_TOKEN)
-
-# Global State
+# Global status dictionary
 processing_status = {
     "is_running": False,
-    "current_file": None,
-    "total_files": 0,
-    "processed_files": 0,
-    "failed_files": 0,
-    "extracted_courses": 0,
-    "extracted_videos": 0,
-    "extracted_frames_count": 0,
-    "analyzed_frames_count": 0,
-    "last_update": None,
-    "logs": []
-}
-
-import torch
-import subprocess
-import sys
-
-device = "cpu" # Explicitly ensure CPU usage
-
-try:
-    # Load processor with padding configuration
-    vision_processor = AutoProcessor.from_pretrained(
-        "microsoft/git-base-coco",
-        padding="max_length",
-        truncation=True
-    )
-    
-    # Load model with CPU optimizations
-    vision_model = AutoModelForCausalLM.from_pretrained(
-        "microsoft/git-base-coco",
-        torch_dtype=torch.float32,
-        low_cpu_mem_usage=True,
-        device_map="cpu"
-    ).eval()
-    
-    print("✅ Successfully loaded GIT model and processor")
-    
-except Exception as e:
-    print(f"❌ Error loading model: {str(e)}")
-    vision_model = None
-    vision_processor = None
-
-# Preprompt templates
-PREPROMPT_TEMPLATES = {
-    "default": "This image shows: ",
-    "design": "This design tutorial frame shows: ",
-    "ui": "This user interface demonstrates: ",
-    "motion": "This motion design example illustrates: "
+    "current_step": "Idle",
+    "progress": 0,  # Percentage
+    "total_videos_processed": 0,
+    "current_video": "N/A",
+    "logs": [],
+    "last_update": datetime.now().isoformat()
 }
 
-def get_preprompt(video_filename: str) -> str:
-    """Select appropriate preprompt based on video content"""
-    filename = video_filename.lower()
-    if any(x in filename for x in ["ui", "interface", "ux"]):
-        return PREPROMPT_TEMPLATES["ui"]
-    elif any(x in filename for x in ["design", "tutorial"]):
-        return PREPROMPT_TEMPLATES["design"]
-    elif any(x in filename for x in ["motion", "animation"]):
-        return PREPROMPT_TEMPLATES["motion"]
-    return PREPROMPT_TEMPLATES["default"]
+# Configuration
+VIDEO_INPUT_FOLDER = "./input_videos"
+RAR_INPUT_FOLDER = "./input_rars"
+FRAME_OUTPUT_FOLDER = "./output_frames"
+ANALYSIS_OUTPUT_FOLDER = "./output_analysis"
 
-def log_message(message: str):
-    """Log messages with timestamp"""
-    timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
+def log_message(message):
+    """Add a log message with timestamp"""
+    timestamp = datetime.now().strftime("%H:%M:%S")
     log_entry = f"[{timestamp}] {message}"
-    print(log_entry)
     processing_status["logs"].append(log_entry)
-    processing_status["last_update"] = timestamp
+    
+    # Keep only the last 100 logs
     if len(processing_status["logs"]) > 100:
         processing_status["logs"] = processing_status["logs"][-100:]
-
-def log_failed_file(filename: str, error: str):
-    """Log failed files to persistent file"""
-    with open(FAILED_FILES_LOG, "a") as f:
-        f.write(f'{time.strftime("%Y-%m-%d %H:%M:%S")} - {filename}: {error}\n')
-
-def get_disk_usage(path: str) -> Dict[str, float]:
-    """Get disk usage statistics in GB"""
-    statvfs = os.statvfs(path)
-    total = statvfs.f_frsize * statvfs.f_blocks / (1024**3)
-    free = statvfs.f_frsize * statvfs.f_bavail / (1024**3)
-    used = total - free
-    return {"total": total, "free": free, "used": used}
-
-def check_disk_space(path: str = ".") -> bool:
-    """Check if there\'s enough disk space"""
-    disk_info = get_disk_usage(path)
-    if disk_info["free"] < MIN_FREE_SPACE_GB:
-        log_message(f'⚠️ Low disk space: {disk_info["free"]:.2f}GB free, {disk_info["used"]:.2f}GB used')
-        return False
-    return True
-
-def cleanup_temp_files():
-    """Clean up temporary files to free space"""
-    log_message("🧹 Cleaning up temporary files...")
     
-    # Clean old downloads (keep only current processing file)
-    current_file = processing_status.get("current_file")
-    for file in os.listdir(DOWNLOAD_FOLDER):
-        if file != current_file and file.endswith((".rar", ".zip")):
-            try:
-                os.remove(os.path.join(DOWNLOAD_FOLDER, file))
-                log_message(f"🗑️ Removed old download: {file}")
-            except:
-                pass
-
-def load_json_state(file_path: str, default_value):
-    """Load state from JSON file"""
-    if os.path.exists(file_path):
-        try:
-            with open(file_path, "r") as f:
-                return json.load(f)
-        except json.JSONDecodeError:
-            log_message(f"⚠️ Corrupted state file: {file_path}")
-    return default_value
-
-def save_json_state(file_path: str, data):
-    """Save state to JSON file"""
-    with open(file_path, "w") as f:
-        json.dump(data, f, indent=2)
+    print(log_entry)
 
-def download_with_retry(url: str, dest_path: str, max_retries: int = 3) -> bool:
-    """Download file with retry logic and disk space checking"""
-    if not check_disk_space():
-        cleanup_temp_files()
-        if not check_disk_space():
-            log_message("❌ Insufficient disk space even after cleanup")
-            return False
+def update_status(step, progress, current_video="N/A"):
+    """Update the global processing status"""
+    processing_status["current_step"] = step
+    processing_status["progress"] = progress
+    processing_status["current_video"] = current_video
+    processing_status["last_update"] = datetime.now().isoformat()
+    log_message(f"Status: {step} - {progress}% for {current_video}")
+
+def extract_rar_files(rar_folder, video_output_folder):
+    """Extracts RAR files to the specified video output folder."""
+    os.makedirs(video_output_folder, exist_ok=True)
+    rar_files = [f for f in os.listdir(rar_folder) if f.endswith('.rar')]
+    total_rars = len(rar_files)
     
-    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-    for attempt in range(max_retries):
-        try:
-            with requests.get(url, headers=headers, stream=True) as r:
-                r.raise_for_status()
-                
-                # Check content length if available
-                content_length = r.headers.get("content-length")
-                if content_length:
-                    size_gb = int(content_length) / (1024**3)
-                    disk_info = get_disk_usage(".")
-                    if size_gb > disk_info["free"] - 0.5:  # Leave 0.5GB buffer
-                        log_message(f'❌ File too large: {size_gb:.2f}GB, only {disk_info["free"]:.2f}GB free')
-                        return False
-                
-                with open(dest_path, "wb") as f:
-                    for chunk in r.iter_content(chunk_size=8192):
-                        f.write(chunk)
-            return True
-        except Exception as e:
-            if attempt < max_retries - 1:
-                time.sleep(2 ** attempt)
-                continue
-            log_message(f"❌ Download failed after {max_retries} attempts: {e}")
-            return False
-    return False
-
-def is_multipart_rar(filename: str) -> bool:
-    """Check if this is a multi-part RAR file"""
-    return ".part" in filename.lower() and filename.lower().endswith(".rar")
+    if total_rars == 0:
+        log_message("No RAR files found to extract.")
+        return
 
-def get_rar_part_base(filename: str) -> str:
-    """Get the base name for multi-part RAR files"""
-    if ".part" in filename.lower():
-        return filename.split(".part")[0]
-    return filename.replace(".rar", "")
-
-def extract_with_retry(rar_path: str, output_dir: str, max_retries: int = 2) -> bool:
-    """Extract RAR with retry and recovery, handling multi-part archives"""
-    filename = os.path.basename(rar_path)
-    
-    # For multi-part RARs, we need the first part
-    if is_multipart_rar(filename):
-        base_name = get_rar_part_base(filename)
-        first_part = f"{base_name}.part01.rar"
-        first_part_path = os.path.join(os.path.dirname(rar_path), first_part)
+    for i, rar_file in enumerate(rar_files):
+        if not processing_status["is_running"]:
+            log_message("RAR extraction interrupted.")
+            return
         
-        if not os.path.exists(first_part_path):
-            log_message(f"⚠️ Multi-part RAR detected but first part not found: {first_part}")
-            return False
+        rar_path = os.path.join(rar_folder, rar_file)
+        log_message(f"Extracting {rar_file}...")
+        update_status("Extracting RAR files", int((i / total_rars) * 100), rar_file)
         
-        rar_path = first_part_path
-        log_message(f"📦 Processing multi-part RAR starting with: {first_part}")
-    
-    for attempt in range(max_retries):
         try:
-            # Test RAR first
-            test_cmd = ["unrar", "t", rar_path]
-            test_result = subprocess.run(test_cmd, capture_output=True, text=True)
-            if test_result.returncode != 0:
-                log_message(f"⚠️ RAR test failed: {test_result.stderr}")
-                if attempt == max_retries - 1:
-                    return False
-                continue
-            
-            # Extract RAR
-            cmd = ["unrar", "x", "-o+", rar_path, output_dir]
-            if attempt > 0:  # Try recovery on subsequent attempts
-                cmd.insert(2, "-kb")
-            
-            result = subprocess.run(cmd, capture_output=True, text=True)
-            if result.returncode == 0:
-                log_message(f"✅ Successfully extracted: {os.path.basename(rar_path)}")
-                return True
-            else:
-                error_msg = result.stderr or result.stdout
-                log_message(f"⚠️ Extraction attempt {attempt + 1} failed: {error_msg}")
-                
-                if "checksum error" in error_msg.lower() or "CRC failed" in error_msg:
-                    log_message(f"⚠️ Data corruption detected, attempt {attempt + 1}")
-                elif result.returncode == 10:
-                    log_message(f"⚠️ No files to extract (exit code 10)")
-                    return False
-                elif result.returncode == 1:
-                    log_message(f"⚠️ Non-fatal error (exit code 1)")
-                    
-        except Exception as e:
-            log_message(f"❌ Extraction exception: {str(e)}")
-            if attempt == max_retries - 1:
-                return False
-            time.sleep(1)
-    
-    return False
-
-def ensure_dir(path):
-    os.makedirs(path, exist_ok=True)
-
-def extract_frames(video_path, output_dir, fps=DEFAULT_FPS):
-    """Extract frames from video at the specified frames per second (fps)."""
-    log_message(f"[INFO] Extracting frames from {video_path} to {output_dir} at {fps} fps...")
-    ensure_dir(output_dir)
-    cap = cv2.VideoCapture(str(video_path))
-    if not cap.isOpened():
-        log_message(f"[ERROR] Failed to open video file: {video_path}")
-        return 0
-    video_fps = cap.get(cv2.CAP_PROP_FPS)
-    if not video_fps or video_fps <= 0:
-        video_fps = 30  # fallback if FPS is not available
-        log_message(f"[WARN] Using fallback FPS: {video_fps}")
-    frame_interval = int(round(video_fps / fps))
-    frame_idx = 0
-    saved_idx = 1
-    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    log_message(f"[DEBUG] Total frames in video: {total_frames}")
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        if frame_idx % frame_interval == 0:
-            if saved_idx <= 10: # Limit to 10 frames for testing
-                frame_name = f"{saved_idx:04d}.png"
-                cv2.imwrite(str(Path(output_dir) / frame_name), frame)
-                saved_idx += 1
-            else:
-                break # Stop extracting after 10 frames
-        frame_idx += 1
-    cap.release()
-    log_message(f"Extracted {saved_idx-1} frames from {video_path} to {output_dir}")
-    return saved_idx - 1
-
-def analyze_single_frame(image_path: str, preprompt: str = "") -> dict:
-    """Consistent frame processing function with robust error handling"""
-    if not vision_model or not vision_processor:
-        return {
-            "image": os.path.basename(image_path),
-            "description": "[ERROR] Model not loaded",
-            "success": False
-        }
-
-    try:
-        # Load and resize image
-        image = Image.open(image_path).convert("RGB")
-        image = image.resize((224, 224))
-
-        # Ensure tokenizer padding config is safe
-        tokenizer = vision_processor.tokenizer
-        if tokenizer.pad_token is None:
-            tokenizer.pad_token = tokenizer.eos_token
-        tokenizer.padding_side = "right"
-
-        # Preprocess inputs
-        inputs = vision_processor(
-            images=[image],
-            text=preprompt,
-            return_tensors="pt",
-            padding="max_length",
-            truncation=True,
-            max_length=512
-        ).to(device)
-
-        # Safety: check pixel_values shape
-        pixel_values = inputs["pixel_values"]
-        if pixel_values.dim() == 3:
-            pixel_values = pixel_values.unsqueeze(0)
-
-        # Generate caption
-        with torch.no_grad():
-            outputs = vision_model.generate(
-                input_ids=inputs["input_ids"],
-                attention_mask=inputs["attention_mask"],
-                pixel_values=pixel_values,
-                max_new_tokens=500,
-                num_beams=5,
-                early_stopping=False,
-                pad_token_id=tokenizer.pad_token_id
-            )
-
-        caption = vision_processor.batch_decode(
-            outputs,
-            skip_special_tokens=True
-        )[0].strip()
-
-        return {
-            "image": os.path.basename(image_path),
-            "description": caption,
-            "success": True
-        }
-
-    except Exception as e:
-        return {
-            "image": os.path.basename(image_path),
-            "description": f"[ERROR] {str(e)}",
-            "success": False
-        }
-
+            # Using 'bsdtar' which is commonly available and handles RAR
+            subprocess.run(['bsdtar', '-xf', rar_path, '-C', video_output_folder], check=True)
+            log_message(f"Successfully extracted {rar_file}")
+        except subprocess.CalledProcessError as e:
+            log_message(f"Error extracting {rar_file}: {e}")
+        except FileNotFoundError:
+            log_message("Error: 'bsdtar' command not found. Please install it (e.g., sudo apt-get install bsdtar).")
+            processing_status["is_running"] = False # Stop processing if essential tool is missing
+            return
 
-def process_video_frames(frames_dir: str, video_filename: str, output_file: str) -> bool:
-    """Main processing function with first-frame validation"""
+def extract_frames(video_path, output_folder):
+    """Extracts frames from a video using ffmpeg."""
+    video_name = Path(video_path).stem
+    frame_output_path = os.path.join(output_folder, video_name)
+    os.makedirs(frame_output_path, exist_ok=True)
+    
+    log_message(f"Extracting frames from {video_path} to {frame_output_path}")
+    command = [
+        'ffmpeg',
+        '-i', video_path,
+        '-vf', 'fps=1',
+        f'{frame_output_path}/frame_%04d.png'
+    ]
+    
     try:
-        frames = sorted(Path(frames_dir).glob("*.png"))
-        if not frames:
-            print("❌ No frames found in directory")
-            return False
-
-        # Validate first frame
-        first_frame_result = analyze_single_frame(str(frames[0]), get_preprompt(video_filename))
-        print("\n=== FIRST FRAME VALIDATION ===")
-        print(f'Image: {first_frame_result["image"]}')
-        print(f'Result: {first_frame_result["description"]}')
-        print(f'Status: {"Success" if first_frame_result["success"] else "Failed"}\n')
-
-        if not first_frame_result["success"]:
-            print("❌ Aborting due to first frame failure")
-            return False
-
-        preprompt = get_preprompt(video_filename)
-        results = {
-            "metadata": {
-                "video": video_filename,
-                "preprompt": preprompt,
-                "total_frames": len(frames),
-                "processed_frames": 0,
-                "failed_frames": 0
-            },
-            "frames": []
-        }
-        
-        for i, frame_path in enumerate(frames):
-            result = analyze_single_frame(str(frame_path), preprompt)
-            results["frames"].append(result)
-            
-            if result["success"]:
-                results["metadata"]["processed_frames"] += 1
-            else:
-                results["metadata"]["failed_frames"] += 1
-            
-            # Periodic saving
-            if i % 10 == 0:
-                with open(output_file, "w") as f:
-                    json.dump(results, f, indent=2)
-
-        # Final save
-        with open(output_file, "w") as f:
-            json.dump(results, f, indent=2)
-            
-        return True
-
-    except Exception as e:
-        print(f"❌ Processing failed: {str(e)}")
-        return False
-
-def summarize_activities(frame_analyses: List[Dict]) -> Dict:
-    """Summarize activities from frame analyses."""
-    return {}
+        subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        log_message(f"Finished extracting frames for {video_name}")
+        return frame_output_path
+    except subprocess.CalledProcessError as e:
+        log_message(f"Error extracting frames from {video_name}: {e.stderr.decode()}")
+        return None
+    except FileNotFoundError:
+        log_message("Error: 'ffmpeg' command not found. Please install ffmpeg.")
+        processing_status["is_running"] = False
+        return None
 
-def process_rar_file(rar_path: str) -> bool:
-    """Process a single RAR file with new frame processing"""
-    filename = os.path.basename(rar_path)
-    processing_status["current_file"] = filename
+def analyze_frame(frame_path):
+    """Simulates frame analysis and returns dummy data."""
+    # In a real scenario, this would involve ML models (e.g., YOLO, CLIP, custom models)
+    # For demonstration, we return a dummy analysis.
     
-    # Handle multi-part RAR naming
-    if is_multipart_rar(filename):
-        course_name = get_rar_part_base(filename)
-    else:
-        course_name = filename.replace(".rar", "")
+    # Simulate some processing time
+    time.sleep(0.1) 
     
-    extract_dir = os.path.join(EXTRACT_FOLDER, course_name)
+    frame_id = Path(frame_path).stem
+    dummy_analysis = {
+        "frame_id": frame_id,
+        "timestamp": datetime.now().isoformat(),
+        "objects_detected": [
+            {"label": "person", "confidence": 0.95, "bbox": [10, 20, 30, 40]},
+            {"label": "car", "confidence": 0.80, "bbox": [50, 60, 70, 80]}
+        ],
+        "description": f"A {frame_id} showing various objects."
+    }
+    return dummy_analysis
+
+def perform_vision_analysis(frame_folder):
+    """Performs vision analysis on all frames in a folder."""
+    if not frame_folder or not os.path.exists(frame_folder):
+        log_message(f"Frame folder {frame_folder} not found for analysis.")
+        return None
+
+    frames = sorted([f for f in os.listdir(frame_folder) if f.endswith('.png')])
+    total_frames = len(frames)
+    video_name = Path(frame_folder).name
     
-    try:
-        log_message(f"🔄 Processing: {filename}")
-        
-        # Clean up any existing directory
-        if os.path.exists(extract_dir):
-            shutil.rmtree(extract_dir, ignore_errors=True)
-        
-        # Extract RAR
-        os.makedirs(extract_dir, exist_ok=True)
-        if not extract_with_retry(rar_path, extract_dir):
-            raise Exception("RAR extraction failed")
+    if total_frames == 0:
+        log_message(f"No frames found in {frame_folder} for analysis.")
+        return None
+
+    analysis_results = []
+    for i, frame_file in enumerate(frames):
+        if not processing_status["is_running"]:
+            log_message("Vision analysis interrupted.")
+            break
         
-        # Process video files
-        video_files = []
-        for root, _, files in os.walk(extract_dir):
-            for file in files:
-                if file.lower().endswith((".mp4", ".avi", ".mov", ".mkv")):
-                    video_files.append(os.path.join(root, file))
+        frame_path = os.path.join(frame_folder, frame_file)
+        update_status("Performing Vision Analysis", int((i / total_frames) * 100), video_name)
         
-        processing_status["extracted_courses"] += 1
-        log_message(f"✅ Extracted {len(video_files)} videos from \'{course_name}\'")
-
-        # Process each video
-        for video_path in video_files:
-            video_filename = Path(video_path).name
-            video_filename_clean = video_filename.replace(".", "_")
-            frames_dir = os.path.join(FRAMES_OUTPUT_FOLDER, f"{course_name}_{video_filename_clean}_frames")
-            ensure_dir(frames_dir)
-            
-            # Extract frames
-            extracted_count = extract_frames(video_path, frames_dir, DEFAULT_FPS)
-            if extracted_count == 0:
-                raise Exception(f"No frames extracted from {video_filename}")
-            
-            processing_status["extracted_frames_count"] += extracted_count
+        analysis = analyze_frame(frame_path)
+        if analysis:
+            analysis_results.append(analysis)
             
-            # Analyze frames
-            video_filename_clean = video_filename.replace(".", "_")
-            analysis_output = os.path.join(ANALYSIS_OUTPUT_FOLDER, f"{course_name}_{video_filename_clean}_analysis.json")
-            if process_video_frames(frames_dir, video_filename, analysis_output):
-                processing_status["analyzed_frames_count"] += extracted_count
-                processing_status["extracted_videos"] += 1
-            else:
-                raise Exception(f"Frame analysis failed for {video_filename}")
-            
-        return True
-    
-    except Exception as e:
-        error_msg = str(e)
-        log_message(f"❌ Processing failed: {error_msg}")
-        log_failed_file(filename, error_msg)
-        return False
+    # Dummy summary generation
+    summary = {
+        "total_frames_analyzed": len(analysis_results),
+        "avg_objects_per_frame": sum(len(a['objects_detected']) for a in analysis_results) / len(analysis_results) if analysis_results else 0,
+        "dominant_objects": "person, car",
+        "high_level_goal": "Identify key activities",
+        "final_goal": "Generate comprehensive video report",
+        "steps": ["Frame extraction", "Object detection", "Activity recognition"]
+    }
+
+    return {"video_name": video_name, "frame_analyses": analysis_results, "summary": summary}
+
+def save_analysis_results(analysis_data, output_folder):
+    """Saves the analysis results to a JSON file."""
+    os.makedirs(output_folder, exist_ok=True)
+    video_name = analysis_data["video_name"]
+    output_path = os.path.join(output_folder, f"{video_name}_analysis.json")
     
-    finally:
-        processing_status["current_file"] = None
+    with open(output_path, 'w') as f:
+        json.dump(analysis_data, f, indent=4)
+    log_message(f"Analysis results saved to {output_path}")
+    return output_path
 
 def main_processing_loop(start_index: int = 0):
-    """Main processing workflow - extraction, frame extraction, and vision analysis"""
+    """Main function to orchestrate the video processing pipeline."""
     processing_status["is_running"] = True
-    
+    processing_status["total_videos_processed"] = 0
+    processing_status["logs"] = [] # Clear logs on new run
+    log_message("Starting main processing loop...")
+
     try:
-        # Load state
-        processed_rars = load_json_state(PROCESS_STATE_FILE, {"processed_rars": []})["processed_rars"]
-        download_state = load_json_state(DOWNLOAD_STATE_FILE, {"next_download_index": 0})
-        
-        # Use start_index if provided, otherwise use the saved state
-        next_index = start_index if start_index > 0 else download_state["next_download_index"]
-        
-        log_message(f"📊 Starting from index {next_index}")
-        log_message(f"📊 Previously processed: {len(processed_rars)} files")
-        
-        # Get file list
-        try:
-            files = list(hf_api.list_repo_files(repo_id=SOURCE_REPO_ID, repo_type="dataset"))
-            rar_files = sorted([f for f in files if f.endswith(".rar")])
-            
-            processing_status["total_files"] = len(rar_files)
-            log_message(f"📁 Found {len(rar_files)} RAR files in repository")
-            
-            if next_index >= len(rar_files):
-                log_message("✅ All files have been processed!")
-                return
-                
-        except Exception as e:
-            log_message(f"❌ Failed to get file list: {str(e)}")
+        # Step 1: Extract RAR files
+        update_status("Starting RAR Extraction", 0)
+        extract_rar_files(RAR_INPUT_FOLDER, VIDEO_INPUT_FOLDER)
+        if not processing_status["is_running"]:
             return
-        
-        # Process only one file per run
-        if next_index < len(rar_files):
-            rar_file = rar_files[next_index]
-            filename = os.path.basename(rar_file)
-            
-            if filename in processed_rars:
-                log_message(f"⏭️ Skipping already processed: {filename}")
-                processing_status["processed_files"] += 1
-                # Move to next file
-                next_index += 1
-                save_json_state(DOWNLOAD_STATE_FILE, {"next_download_index": next_index})
-                log_message(f"📊 Moving to next file. Progress: {next_index}/{len(rar_files)}")
-                return
-            
-            log_message(f"📥 Downloading: {filename}")
-            dest_path = os.path.join(DOWNLOAD_FOLDER, filename)
-            
-            # Download file
-            download_url = f"https://huggingface.co/datasets/{SOURCE_REPO_ID}/resolve/main/{rar_file}"
-            if download_with_retry(download_url, dest_path):
-                # Process file
-                if process_rar_file(dest_path):
-                    processed_rars.append(filename)
-                    save_json_state(PROCESS_STATE_FILE, {"processed_rars": processed_rars})
-                    log_message(f"✅ Successfully processed: {filename}")
-                    processing_status["processed_files"] += 1
-                else:
-                    log_message(f"❌ Failed to process: {filename}")
-                    processing_status["failed_files"] += 1
-                
-                # Clean up downloaded file
-                try:
-                    os.remove(dest_path)
-                    log_message(f"🗑️ Cleaned up download: {filename}")
-                except:
-                    pass
-            else:
-                log_message(f"❌ Failed to download: {filename}")
-                processing_status["failed_files"] += 1
-            
-            # Update download state for next run
-            next_index += 1
-            save_json_state(DOWNLOAD_STATE_FILE, {"next_download_index": next_index})
-            
-            # Status update
-            log_message(f"📊 Progress: {next_index}/{len(rar_files)} files processed")
-            log_message(f"📊 Extracted: {processing_status['extracted_courses']} courses")
-            log_message(f"📊 Videos Processed: {processing_status['extracted_videos']} videos")
-            log_message(f"📊 Frames Extracted: {processing_status['extracted_frames_count']} frames")
-            log_message(f"📊 Frames Analyzed: {processing_status['analyzed_frames_count']} frames")
-            log_message(f"📊 Failed: {processing_status['failed_files']} files")
-            
-            if next_index < len(rar_files):
-                log_message(f"🔄 Run the script again to process the next file: {os.path.basename(rar_files[next_index])}")
-            else:
-                log_message("🎉 All files have been processed!")
-        else:
-            log_message("✅ All files have been processed!")
-        
-        log_message(f"🎉 Processing complete!")
-        log_message(f"📊 Final stats: {processing_status['extracted_courses']} courses extracted, {processing_status['extracted_videos']} videos processed, {processing_status['extracted_frames_count']} frames extracted, {processing_status['analyzed_frames_count']} frames analyzed")
-        
-    except KeyboardInterrupt:
-        log_message("⏹️ Processing interrupted by user")
-    except Exception as e:
-        log_message(f"❌ Fatal error: {str(e)}")
-    finally:
-        processing_status["is_running"] = False
-        cleanup_temp_files()
+        log_message("RAR extraction complete.")
 
-# FastAPI Endpoints
-@app.post("/analyze-video")
-async def analyze_video_endpoint(
-    file: UploadFile = File(...),
-    fps: float = Form(DEFAULT_FPS),
-    prompt: Optional[str] = Form(None)
-):
-    """Analyze a single video file and return frame-by-frame analysis."""
-    if not file.filename.lower().endswith((".mp4", ".avi", ".mov", ".mkv")):
-        return JSONResponse(status_code=400, content={
-            "error": "File type not allowed",
-            "allowed_types": [".mp4", ".avi", ".mov", ".mkv"]
-        })
-    
-    with tempfile.TemporaryDirectory() as temp_dir:
-        temp_dir_path = Path(temp_dir)
-        file_path = temp_dir_path / file.filename
-        
-        with open(file_path, "wb") as buffer:
-            shutil.copyfileobj(file.file, buffer)
+        # Step 2: Process videos
+        video_files = [f for f in os.listdir(VIDEO_INPUT_FOLDER) if f.endswith(('.mp4', '.avi', '.mov', '.mkv'))]
+        total_videos = len(video_files)
         
-        frames_dir = temp_dir_path / "frames"
-        frame_count = extract_frames(file_path, frames_dir, fps)
-        
-        frame_analyses = []
-        for frame_file in sorted(frames_dir.glob("*.png")):
-            analysis = analyze_single_frame(str(frame_file), prompt or "")
-            frame_analyses.append(analysis)
-        
-        summary = summarize_activities(frame_analyses)
-        
-        return JSONResponse(content={
-            "video_filename": file.filename,
-            "frame_count": frame_count,
-            "fps": fps,
-            "frame_analyses": frame_analyses,
-            "summary": summary
-        })
+        if total_videos == 0:
+            log_message("No video files found to process.")
+            update_status("Finished", 100)
+            processing_status["is_running"] = False
+            return
 
-@app.post("/analyze-archive")
-async def analyze_archive_endpoint(
-    file: UploadFile = File(...),
-    fps: float = Form(DEFAULT_FPS),
-    prompt: Optional[str] = Form(None)
-):
-    """Analyze videos from RAR/ZIP archive and return frame-by-frame analysis."""
-    if not file.filename.lower().endswith((".rar", ".zip")):
-        return JSONResponse(status_code=400, content={
-            "error": "File type not allowed",
-            "allowed_types": [".rar", ".zip"]
-        })
-    
-    with tempfile.TemporaryDirectory() as temp_dir:
-        temp_dir_path = Path(temp_dir)
-        file_path = temp_dir_path / file.filename
-        
-        with open(file_path, "wb") as buffer:
-            shutil.copyfileobj(file.file, buffer)
-        
-        extract_dir = temp_dir_path / "extracted"
-        video_files = []
-        
-        if file.filename.lower().endswith(".rar"):
-            with rarfile.RarFile(file_path) as rf:
-                rf.extractall(extract_dir)
-        else:
-            with zipfile.ZipFile(file_path) as zf:
-                zf.extractall(extract_dir)
-        
-        # Find video files in extracted content
-        for root, dirs, files in os.walk(extract_dir):
-            for file in files:
-                if file.lower().endswith((".mp4", ".avi", ".mov", ".mkv")):
-                    video_files.append(Path(root) / file)
-        
-        if not video_files:
-            return JSONResponse(status_code=400, content={
-                "error": "No video files found in archive"
-            })
-        
-        results = []
-        for video_path in video_files:
-            video_name = video_path.name
-            frames_dir = temp_dir_path / f"frames_{video_name}"
-            frame_count = extract_frames(video_path, frames_dir, fps)
-            
-            frame_analyses = []
-            for frame_file in sorted(frames_dir.glob("*.png")):
-                analysis = analyze_single_frame(str(frame_file), prompt or "")
-                frame_analyses.append(analysis)
+        for i, video_file in enumerate(video_files):
+            if not processing_status["is_running"]:
+                log_message("Video processing interrupted.")
+                break
             
-            summary = summarize_activities(frame_analyses)
-            
-            results.append({
-                "video_filename": video_name,
-                "frame_count": frame_count,
-                "fps": fps,
-                "frame_analyses": frame_analyses,
-                "summary": summary
-            })
-        
-        return JSONResponse(content={
-            "archive_filename": file.filename,
-            "videos_processed": len(video_files),
-            "results": results
-        })
+            if i < start_index:
+                log_message(f"Skipping video {video_file} due to start_index.")
+                continue
+
+            video_path = os.path.join(VIDEO_INPUT_FOLDER, video_file)
+            log_message(f"Processing video: {video_file}")
+            processing_status["current_video"] = video_file
 
-@app.get("/health")
-async def health_check():
-    """Health check endpoint."""
-    return JSONResponse(content={
-        "status": "healthy",
-        "model": "GIT",
-        "note": "Now using GIT model."
-    })
+            # Extract frames
+            update_status("Extracting Frames", 0, video_file)
+            frame_folder = extract_frames(video_path, FRAME_OUTPUT_FOLDER)
+            if not frame_folder:
+                log_message(f"Skipping analysis for {video_file} due to frame extraction failure.")
+                continue
+            if not processing_status["is_running"]:
+                break
+
+            # Perform vision analysis
+            update_status("Performing Vision Analysis", 0, video_file)
+            analysis_data = perform_vision_analysis(frame_folder)
+            if not analysis_data:
+                log_message(f"Skipping saving results for {video_file} due to analysis failure.")
+                continue
+            if not processing_status["is_running"]:
+                break
 
-@app.get("/status")
-async def get_processing_status():
-    """Get current processing status."""
-    return JSONResponse(content=processing_status)
+            # Save results
+            update_status("Saving Analysis Results", 0, video_file)
+            save_analysis_results(analysis_data, ANALYSIS_OUTPUT_FOLDER)
+            
+            processing_status["total_videos_processed"] += 1
+            log_message(f"Finished processing {video_file}")
+            
+            # Update overall progress
+            overall_progress = int(((i + 1) / total_videos) * 100)
+            update_status("Processing Videos", overall_progress, video_file)
 
-# Expose necessary functions and variables
-__all__ = [
-    "main_processing_loop",
-    "processing_status",
-    "ANALYSIS_OUTPUT_FOLDER",
-    "log_message",
-    "analyze_single_frame",
-    "extract_frames",
-    "DEFAULT_FPS",
-    "ensure_dir"
-]
+    except Exception as e:
+        log_message(f"An error occurred in the main processing loop: {e}")
+    finally:
+        processing_status["is_running"] = False
+        processing_status["current_step"] = "Finished" if processing_status["is_running"] else "Stopped"
+        processing_status["progress"] = 100 if processing_status["is_running"] else processing_status["progress"]
+        log_message("Main processing loop finished or stopped.")
+
+if __name__ == "__main__":
+    # Example usage: Ensure folders exist and place dummy files
+    os.makedirs(RAR_INPUT_FOLDER, exist_ok=True)
+    os.makedirs(VIDEO_INPUT_FOLDER, exist_ok=True)
+    os.makedirs(FRAME_OUTPUT_FOLDER, exist_ok=True)
+    os.makedirs(ANALYSIS_OUTPUT_FOLDER, exist_ok=True)
+
+    # Create a dummy RAR file for testing
+    # with open(os.path.join(RAR_INPUT_FOLDER, "dummy.rar"), "w") as f:
+    #     f.write("This is a dummy rar file.")
+
+    # Create a dummy video file for testing (requires ffmpeg to extract frames)
+    # You can create a small dummy mp4 using ffmpeg:
+    # ffmpeg -f lavfi -i color=c=red:s=320x240:d=1 -c:v libx264 -preset superfast -crf 23 dummy_video.mp4
+    # Or just create an empty file as a placeholder
+    # with open(os.path.join(VIDEO_INPUT_FOLDER, "dummy_video.mp4"), "w") as f:
+    #     f.write("This is a dummy video file.")
+
+    print("Setup complete. You can now run the FastAPI/Gradio app.")
+    # To run the processing loop directly (for testing without FastAPI/Gradio):
+    # main_processing_loop()