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#!/usr/bin/env python3
"""
Improved quick test script for Sanskrit multimodal model
Uses better prompting to get actual Sanskrit text transcription
"""
import json
import torch
import base64
import io
from PIL import Image
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
from peft import PeftModel
import numpy as np
from typing import List, Dict
import re
import os
def load_model_and_processor(model_path: str, adapter_path: str = None):
"""Load the base model and processor"""
print("Loading processor...")
processor = AutoProcessor.from_pretrained(model_path)
print("Loading base model...")
# Use the correct Qwen2.5-VL model class
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map={"": 0} # Force to GPU 0
)
if adapter_path and os.path.exists(adapter_path):
print("Loading LoRA adapters...")
model = PeftModel.from_pretrained(model, adapter_path)
else:
print("No adapter path found, using base model only")
model.eval()
print(f"Model loaded on device: {next(model.parameters()).device}")
return model, processor
def decode_base64_image(base64_string: str) -> Image.Image:
"""Decode base64 string to PIL Image"""
if base64_string.startswith('data:image'):
base64_string = base64_string.split(',')[1]
image_data = base64.b64decode(base64_string)
image = Image.open(io.BytesIO(image_data))
return image
def preprocess_sanskrit_text(text: str) -> str:
"""Preprocess Sanskrit text for evaluation"""
text = re.sub(r'\s+', ' ', text.strip())
return text
def calculate_exact_match(predicted: str, ground_truth: str) -> bool:
"""Calculate exact match accuracy"""
predicted = preprocess_sanskrit_text(predicted)
ground_truth = preprocess_sanskrit_text(ground_truth)
return predicted == ground_truth
def calculate_character_accuracy(predicted: str, ground_truth: str) -> float:
"""Calculate character-level accuracy using edit distance"""
predicted = preprocess_sanskrit_text(predicted)
ground_truth = preprocess_sanskrit_text(ground_truth)
if not ground_truth:
return 1.0 if not predicted else 0.0
m, n = len(predicted), len(ground_truth)
dp = [[0] * (n + 1) for _ in range(m + 1)]
for i in range(m + 1):
dp[i][0] = i
for j in range(n + 1):
dp[0][j] = j
for i in range(1, m + 1):
for j in range(1, n + 1):
if predicted[i-1] == ground_truth[j-1]:
dp[i][j] = dp[i-1][j-1]
else:
dp[i][j] = 1 + min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1])
edit_distance = dp[m][n]
max_length = max(m, n)
accuracy = 1.0 - (edit_distance / max_length) if max_length > 0 else 1.0
return max(0.0, accuracy)
def calculate_token_jaccard(predicted: str, ground_truth: str) -> float:
"""Calculate token-level Jaccard similarity"""
predicted = preprocess_sanskrit_text(predicted)
ground_truth = preprocess_sanskrit_text(ground_truth)
pred_tokens = set(predicted.split())
gt_tokens = set(ground_truth.split())
if not pred_tokens and not gt_tokens:
return 1.0
intersection = len(pred_tokens & gt_tokens)
union = len(pred_tokens | gt_tokens)
return intersection / union if union > 0 else 0.0
def generate_response(model, processor, image: Image.Image, prompt: str) -> str:
"""Generate response from the model"""
try:
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": prompt}
]
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
# Get model device and move inputs there
model_device = next(model.parameters()).device
inputs = {k: v.to(model_device) for k, v in inputs.items()}
with torch.no_grad():
generated_ids = model.generate(
**inputs,
max_new_tokens=512, # Increased for longer Sanskrit text
do_sample=False,
pad_token_id=processor.tokenizer.eos_token_id,
use_cache=True,
repetition_penalty=1.1
)
# Extract only the generated part
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs['input_ids'], generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return output_text[0] if output_text else ""
except Exception as e:
print(f"Error generating response: {e}")
import traceback
traceback.print_exc()
return ""
def main():
# Configuration
model_path = 'Qwen/Qwen2.5-VL-7B-Instruct'
adapter_path = './outputs/out-qwen2-5-vl'
test_data_path = 'sanskrit_multimodal_test.json'
# Try different prompts to see which works best
prompts = [
# "What Sanskrit script is visible in this image?",
# "Transcribe the Sanskrit text in this image:",
# "Read the Sanskrit text from this image:",
# "What Sanskrit text is written in this image?",
"Please transcribe the Sanskrit text shown in this image:"
]
max_samples = 3 # Test on first 3 samples
print("Loading test data...")
with open(test_data_path, 'r', encoding='utf-8') as f:
test_data = json.load(f)
test_data = test_data[:max_samples]
print(f"Testing on {len(test_data)} samples")
# Load model
model, processor = load_model_and_processor(model_path, adapter_path)
# Test different prompts
for prompt_idx, prompt in enumerate(prompts):
print(f"\n{'='*60}")
print(f"TESTING PROMPT {prompt_idx + 1}: {prompt}")
print(f"{'='*60}")
# Evaluation metrics
exact_matches = 0
character_accuracies = []
token_jaccards = []
failed_predictions = 0
for i, sample in enumerate(test_data):
print(f"\n--- Sample {i+1} ---")
try:
# Extract ground truth
ground_truth = ""
for message in sample['messages']:
if message['role'] == 'assistant':
for content in message['content']:
if content['type'] == 'text':
ground_truth = content['text']
break
break
# Extract and decode image
image = None
for message in sample['messages']:
if message['role'] == 'user':
for content in message['content']:
if content['type'] == 'image':
image = decode_base64_image(content['base64'])
break
break
if image is None:
print("No image found")
failed_predictions += 1
continue
print(f"Ground Truth: {ground_truth}")
# Generate prediction
predicted = generate_response(model, processor, image, prompt)
print(f"Predicted: {predicted}")
if not predicted:
print("Empty prediction")
failed_predictions += 1
continue
# Calculate metrics
exact_match = calculate_exact_match(predicted, ground_truth)
char_accuracy = calculate_character_accuracy(predicted, ground_truth)
token_jaccard = calculate_token_jaccard(predicted, ground_truth)
print(f"Exact Match: {exact_match}")
print(f"Character Accuracy: {char_accuracy:.4f}")
print(f"Token Jaccard: {token_jaccard:.4f}")
if exact_match:
exact_matches += 1
character_accuracies.append(char_accuracy)
token_jaccards.append(token_jaccard)
except Exception as e:
print(f"Error processing sample: {e}")
failed_predictions += 1
continue
# Calculate final results for this prompt
successful_samples = len(test_data) - failed_predictions
if successful_samples > 0:
exact_match_accuracy = exact_matches / successful_samples
avg_char_accuracy = np.mean(character_accuracies)
avg_token_jaccard = np.mean(token_jaccards)
else:
exact_match_accuracy = 0.0
avg_char_accuracy = 0.0
avg_token_jaccard = 0.0
# Print results for this prompt
print(f"\n--- RESULTS FOR PROMPT {prompt_idx + 1} ---")
print(f"Exact Match Accuracy: {exact_match_accuracy:.4f}")
print(f"Average Character Accuracy: {avg_char_accuracy:.4f}")
print(f"Average Token Jaccard: {avg_token_jaccard:.4f}")
print(f"\n{'='*60}")
print("ALL PROMPTS TESTED")
print(f"{'='*60}")
if __name__ == "__main__":
main()
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