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#!/usr/bin/env python3
"""
Test script for video extraction and processing functionality
"""
import os
import sys
import json
from io import BytesIO
import tempfile
# Add current directory to path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from app import extract_frames_from_video, query_huggingface_api, load_settings
def test_video_extraction():
"""Test video extraction with the problematic video file"""
# Find the actual video file in the directory
video_files = [f for f in os.listdir('.') if f.endswith('.mp4')]
if not video_files:
print("No MP4 files found in current directory")
return False
video_path = video_files[0] # Use the first MP4 file found
print(f"Using video file: {video_path}")
print(f"Video size: {os.path.getsize(video_path) / (1024*1024):.1f} MB")
# Create a file-like object for testing
with open(video_path, 'rb') as f:
video_data = f.read()
# Create BytesIO object to simulate uploaded file
video_file = BytesIO(video_data)
print("\nTesting video frame extraction...")
try:
frames = extract_frames_from_video(video_file, fps=0.5) # Extract 1 frame every 2 seconds
if frames:
print(f"Successfully extracted {len(frames)} frames")
for i, frame_data in enumerate(frames[:3]): # Show first 3 frames
print(f" Frame {i}: {frame_data['timestamp']:.1f}s, size: {frame_data['frame'].size}")
return frames
else:
print("No frames extracted")
return None
except Exception as e:
print(f"Error during extraction: {e}")
return None
def test_api_integration(frames):
"""Test Hugging Face API integration"""
if not frames:
print("No frames to test API with")
return
# Load settings
settings = load_settings()
api_token = settings.get('hugging_face_api_token')
if not api_token:
print("No API token found in settings.json")
return
print(f"\nTesting API integration...")
print(f"Using token: {api_token[:10]}...")
# Test with first frame and simple prompt
test_frame = frames[0]['frame']
test_prompt = "Describe what you see in this image"
# Try multiple models
models_to_test = [
"nlpconnect/vit-gpt2-image-captioning",
"Salesforce/blip-image-captioning-base",
"microsoft/git-large-coco"
]
for model in models_to_test:
print(f"\nTesting with model: {model}")
print(f"Prompt: {test_prompt}")
try:
result = query_huggingface_api(test_frame, test_prompt, model, api_token)
if 'error' in result:
print(f"API Error: {result['error']}")
else:
print("API call successful!")
print(f"Result: {result}")
break # Stop on first successful model
except Exception as e:
print(f"Exception during API call: {e}")
continue
def main():
print("Testing Video Frame Analyzer Functionality")
print("=" * 50)
# Test 1: Video extraction
frames = test_video_extraction()
# Test 2: API integration (if frames extracted successfully)
if frames:
test_api_integration(frames)
print("\n" + "=" * 50)
print("Testing complete!")
if __name__ == "__main__":
main() |