Update vision_analyzer.py

vision_analyzer.py

@@ -1,692 +1,748 @@
-import os
-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
-
-# Frame Extraction Parameters
-DEFAULT_FPS = 3  # Default frames per second for extraction
-
-# Initialize HF API
-hf_api = HfApi(token=HF_TOKEN)
-
-# Global State
-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 the model, forcing the 'eager' (CPU-compatible) attention implementation
-    vision_language_model_large = AutoModelForCausalLM.from_pretrained(
-        "microsoft/Florence-2-Base",
-        trust_remote_code=True
-    ).to(device).eval()
-    vision_language_processor_large = AutoProcessor.from_pretrained(
-        "microsoft/Florence-2-Base",
-        trust_remote_code=True
-    )
-    print("Florence-2 large model and processor loaded successfully on CPU using eager attention.")
-except Exception as e:
-    print(f"Error loading Florence-2 model on CPU: {e}")
-    print("Please ensure you have enough RAM and a compatible PyTorch version.")
-    vision_language_model_large = None
-    vision_language_processor_large = None
-
-    
-def log_message(message: str):
-    """Log messages with timestamp"""
-    timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
-    log_entry = f"[{timestamp}] {message}"
-    print(log_entry)
-    processing_status["logs"].append(log_entry)
-    processing_status["last_update"] = timestamp
-    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)
-
-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
-    
-    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")
-
-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)
-        
-        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 = 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:
-            frame_name = f"{saved_idx:04d}.png"
-            cv2.imwrite(str(Path(output_dir) / frame_name), frame)
-            saved_idx += 1
-        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_frame_with_florence2(image_path: str, prompt: str = "<CAPTION>") -> Dict:
-    """Analyze a single frame using Florence-2 vision model."""
-    if not vision_language_model_large or not vision_language_processor_large:
-        return {
-            "image": os.path.basename(image_path),
-            "description": "[ERROR] Vision model not loaded."
-        }
-
-    image = Image.open(image_path).convert("RGB")
-    inputs = vision_language_processor_large(images=image, text=prompt, return_tensors="pt").to(device)
-
-    with torch.no_grad():
-        generated_ids = vision_language_model_large.generate(
-            input_ids=inputs["input_ids"],
-            pixel_values=inputs["pixel_values"],
-            max_new_tokens=512,
-            do_sample=False,
-            num_beams=3
-        )
-
-    generated_text = vision_language_processor_large.batch_decode(generated_ids, skip_special_tokens=False)[0]
-    description = vision_language_processor_large.post_process_generation(
-        generated_text,
-        task="<CAPTION>",
-        image_size=(image.width, image.height)
-    )["<CAPTION>"]
-
-    return {
-        "image": os.path.basename(image_path),
-        "description": description
-    }
-
-def summarize_activities(frame_analyses: List[Dict]) -> Dict:
-    """Summarize activities from frame analyses."""
-    return {
-        # "steps": [
-        #     {
-        #         "action": "Open Blender software",
-        #         "description": "User launches Blender 3D modeling application on their computer"
-        #     },
-        #     {
-        #         "action": "Create 3D object",
-        #         "description": "User works with a default cube object in the 3D viewport"
-        #     },
-        #     {
-        #         "action": "Manipulate 3D model",
-        #         "description": "User rotates and transforms the cube using mouse interactions"
-        #     },
-        #     {
-        #         "action": "Navigate interface",
-        #         "description": "User explores different tools and panels in the Blender interface"
-        #     }
-        # ],
-        # "high_level_goal": "Learning basic 3D modeling operations in Blender software",
-        # "creative_actions": "3D object manipulation, interface navigation, basic modeling workflow",
-        # "objects": ["computer", "monitor", "mouse", "keyboard", "Blender software", "3D cube", "desktop interface"],
-        # "final_goal": "Introduction to Blender 3D modeling fundamentals and basic object manipulation"
-    }
-
-def analyze_frames(frames_dir: str, output_json_path: str, prompt: Optional[str] = None) -> int:
-    """Analyze all frames in directory using Florence-2 model."""
-    log_message(f"[INFO] Analyzing frames in {frames_dir}...")
-    frames_dir = Path(frames_dir).resolve()
-    output_json_path = Path(output_json_path).resolve()
-    ensure_dir(frames_dir)
-    ensure_dir(output_json_path.parent)
-    
-    frame_analyses = []
-    analyzed_count = 0
-    
-    for frame_file in sorted(frames_dir.glob("*.png")):
-        analysis = analyze_frame_with_florence2(str(frame_file), prompt)
-        frame_analyses.append(analysis)
-        analyzed_count += 1
-    
-    # Generate summary
-    summary = summarize_activities(frame_analyses)
-    
-    # Save results
-    results = {
-        "frame_analyses": frame_analyses,
-        "summary": summary
-    }
-    
-    try:
-        with open(output_json_path, "w") as f:
-            json.dump(results, f, indent=2)
-        log_message(f"[SUCCESS] Analysis results saved to {output_json_path}")
-    except Exception as e:
-        log_message(f"[ERROR] Failed to write output JSON: {e}")
-    
-    return analyzed_count
-
-
-def process_rar_file(rar_path: str) -> bool:
-    """Process a single RAR file - extract, then process videos for frames and vision analysis"""
-    filename = os.path.basename(rar_path)
-    processing_status["current_file"] = filename
-    
-    # Handle multi-part RAR naming
-    if is_multipart_rar(filename):
-        course_name = get_rar_part_base(filename)
-    else:
-        course_name = filename.replace(".rar", "")
-    
-    extract_dir = os.path.join(EXTRACT_FOLDER, course_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")
-        
-        # Count extracted files
-        file_count = 0
-        video_files_found = []
-        for root, dirs, files in os.walk(extract_dir):
-            for file in files:
-                file_count += 1
-                if file.lower().endswith((".mp4", ".avi", ".mov", ".mkv")):
-                    video_files_found.append(os.path.join(root, file))
-        
-        processing_status["extracted_courses"] += 1
-        log_message(f"✅ Successfully extracted '{course_name}' ({file_count} files, {len(video_files_found)} videos)")
-
-        # Process video files for frame extraction and vision analysis
-        for video_path in video_files_found:
-            video_filename = Path(video_path).name
-            # Create a unique output directory for frames for each video
-            frames_output_dir = os.path.join(FRAMES_OUTPUT_FOLDER, f"{course_name}_{video_filename.replace('.', '_')}_frames")
-            ensure_dir(frames_output_dir)
-            
-            extracted_frames_count = extract_frames(video_path, frames_output_dir, fps=DEFAULT_FPS)
-            processing_status["extracted_frames_count"] += extracted_frames_count
-            if extracted_frames_count > 0:
-                processing_status["extracted_videos"] += 1
-                log_message(f"[INFO] Extracted {extracted_frames_count} frames from {video_filename}")
-
-                # Perform vision analysis on the extracted frames
-                analysis_output_json = os.path.join(ANALYSIS_OUTPUT_FOLDER, f"{course_name}_{video_filename.replace('.', '_')}_analysis.json")
-                analyzed_frames = analyze_frames(frames_output_dir, analysis_output_json)
-                processing_status["analyzed_frames_count"] += analyzed_frames
-                log_message(f"[INFO] Analyzed {analyzed_frames} frames from {video_filename}")
-            else:
-                log_message(f"[WARN] No frames extracted from {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
-    
-    finally:
-        processing_status["current_file"] = None
-
-def main_processing_loop(start_index: int = 0):
-    """Main processing workflow - extraction, frame extraction, and vision analysis"""
-    processing_status["is_running"] = True
-    
-    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)}")
-            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"]}')
-            log_message(f'📊 Frames Extracted: {processing_status["extracted_frames_count"]}')
-            log_message(f'📊 Frames Analyzed: {processing_status["analyzed_frames_count"]}')
-            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("🎉 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()
-
-# FastAPI Endpoints
-@app.post("/analyze-video")
-async def analyze_video_endpoint(
-    file: UploadFile = File(...),
-    fps: int = 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)
-        
-        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_frame_with_florence2(str(frame_file), prompt)
-            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
-        })
-
-@app.post("/analyze-archive")
-async def analyze_archive_endpoint(
-    file: UploadFile = File(...),
-    fps: int = 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_frame_with_florence2(str(frame_file), prompt)
-                frame_analyses.append(analysis)
-            
-            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
-        })
-
-@app.get("/health")
-async def health_check():
-    """Health check endpoint."""
-    return JSONResponse(content={
-        "status": "healthy",
-        "model": "Florence-2 (Mock)",
-        "note": "Florence-2 model is mocked due to sandbox memory limitations."
-    })
-
-@app.get("/status")
-async def get_processing_status():
-    """Get current processing status."""
-    return JSONResponse(content=processing_status)
-
-# Expose necessary functions and variables
-__all__ = [
-    "main_processing_loop",
-    "processing_status",
-    "ANALYSIS_OUTPUT_FOLDER",
-    "log_message",
-    "send_email_with_attachment",
-    "analyze_frames",
-    "extract_frames",
-    "DEFAULT_FPS",
-    "ensure_dir"
-]
+import os
+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
+
+# Frame Extraction Parameters
+DEFAULT_FPS = 0.1  # Default frames per second for extraction
+
+# Initialize HF API
+hf_api = HfApi(token=HF_TOKEN)
+
+# Global State
+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: "
+}
+
+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"]
+
+def log_message(message: str):
+    """Log messages with timestamp"""
+    timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
+    log_entry = f"[{timestamp}] {message}"
+    print(log_entry)
+    processing_status["logs"].append(log_entry)
+    processing_status["last_update"] = timestamp
+    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)
+
+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
+    
+    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")
+
+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)
+        
+        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 = 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
+        }
+
+
+def process_video_frames(frames_dir: str, video_filename: str, output_file: str) -> bool:
+    """Main processing function with first-frame validation"""
+    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 {}
+
+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
+    
+    # Handle multi-part RAR naming
+    if is_multipart_rar(filename):
+        course_name = get_rar_part_base(filename)
+    else:
+        course_name = filename.replace(".rar", "")
+    
+    extract_dir = os.path.join(EXTRACT_FOLDER, course_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")
+        
+        # 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))
+        
+        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
+            
+            # 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
+    
+    finally:
+        processing_status["current_file"] = None
+
+def main_processing_loop(start_index: int = 0):
+    """Main processing workflow - extraction, frame extraction, and vision analysis"""
+    processing_status["is_running"] = True
+    
+    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)}")
+            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()
+
+# 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)
+        
+        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
+        })
+
+@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)
+            
+            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
+        })
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint."""
+    return JSONResponse(content={
+        "status": "healthy",
+        "model": "GIT",
+        "note": "Now using GIT model."
+    })
+
+@app.get("/status")
+async def get_processing_status():
+    """Get current processing status."""
+    return JSONResponse(content=processing_status)
+
+# 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"
+]
+