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#!/usr/bin/env python3
"""
Test both models with specific instructions like counting
"""
import sys
import os
from io import BytesIO
# Add current directory to path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
def test_instruction_following():
"""Test how well both models follow specific instructions"""
print("Testing Instruction Following")
print("=" * 40)
try:
from local_models import get_local_model_manager
from app import extract_frames_from_video, process_image_locally
print("+ Components imported")
except ImportError as e:
print(f"- Import error: {e}")
return
# Find video file
video_files = [f for f in os.listdir('.') if f.endswith('.mp4')]
if not video_files:
print("- No MP4 files found")
return
video_path = video_files[0]
print(f"+ Using video: {video_path[:40]}...")
# Initialize models
try:
local_manager = get_local_model_manager()
print("+ Models initialized")
except Exception as e:
print(f"- Error: {e}")
return
# Extract a few frames for testing
try:
with open(video_path, 'rb') as f:
video_data = f.read()
video_file = BytesIO(video_data)
frames = extract_frames_from_video(video_file, fps=0.2) # Every 5 seconds
if not frames:
print("- No frames extracted")
return
# Use first 3 frames for testing
test_frames = frames[:3]
print(f"+ Extracted {len(test_frames)} test frames")
except Exception as e:
print(f"- Frame error: {e}")
return
# Test different types of instructions
test_prompts = [
"Count the number of people in this scene",
"How many people are visible?",
"What is the main action happening?",
"Is there a train in this image?",
"Describe the setting"
]
models = ['CNN (BLIP)', 'Transformer (ViT-GPT2)']
for frame_idx, frame_data in enumerate(test_frames):
print(f"\n{'='*50}")
print(f"FRAME {frame_idx + 1} (t={frame_data['timestamp']:.1f}s)")
print(f"{'='*50}")
for prompt in test_prompts:
print(f"\nPrompt: '{prompt}'")
print("-" * 30)
for model in models:
try:
result = process_image_locally(
frame_data['frame'],
prompt,
model,
local_manager
)
if 'error' in result:
response = f"Error: {result['error']}"
else:
response = result.get('generated_text', 'No response')
print(f"{model}: {response}")
except Exception as e:
print(f"{model}: Exception - {e}")
print() # Space between prompts
print("\n" + "=" * 60)
print("INSTRUCTION FOLLOWING ANALYSIS")
print("=" * 60)
print("Key observations to look for:")
print("1. Does CNN avoid repeating the prompt?")
print("2. Do models actually count vs describe?")
print("3. Which model answers questions more directly?")
print("4. How do they handle yes/no questions?")
if __name__ == "__main__":
test_instruction_following() |