Deploy multilingual text recognition app

finetuned_bengali_model.pth

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+size 95408177

parseq

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+Subproject commit 1902db043c029a7e03a3818c616c06600af574be

parseq_oriya_final_direct.pth

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parseq_telugu_finetuned_final_5epochs.pth

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requirements.txt

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+gradio==4.16.0
+torch==2.0.1
+torchvision==0.15.2
+Pillow
+numpy

space.py

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+import gradio as gr
+import torch
+import torchvision.transforms as T
+from PIL import Image
+import os
+import sys
+import warnings
+import logging
+import numpy as np
+
+# Setup logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# =========================
+# Setup PARSeq path
+# =========================
+parseq_path = os.path.join(os.path.dirname(__file__), 'parseq')
+if os.path.exists(parseq_path):
+    sys.path.insert(0, parseq_path)
+else:
+    logger.error(f"PARSeq not found at {parseq_path}")
+    exit()
+
+from strhub.data.utils import Tokenizer
+import torch.hub
+
+warnings.filterwarnings('ignore')
+
+# =========================
+# Configuration
+# =========================
+ORIYA_CHARSET = "ଅଆଇଈଉଊଋଌଏଐଓଔକଖଗଘଙଚଛଜଝଞଟଠଡଢଣତଥଦଧନପଫବଭମଯରଲଳଵଶଷସହାିିୀୁୂୃୄେୈୋୌ୍ଂଁଃ"
+
+LANGUAGES = {
+    "Telugu": {
+        "model_path": "parseq_telugu_finetuned_final_5epochs.pth",
+        "samples_dir": "telugu_samples",
+    },
+    "Bengali": {
+        "model_path": "finetuned_bengali_model.pth",
+        "samples_dir": "bengali_samples",
+    },
+    "Oriya": {
+        "model_path": "parseq_oriya_final_direct.pth",
+        "samples_dir": "oriya_samples",
+        "charset": ORIYA_CHARSET,
+    }
+}
+
+# =========================
+# Image Transform
+# =========================
+transform = T.Compose([
+    T.Resize((32, 128)),
+    T.ToTensor(),
+    T.Normalize(mean=[0.5], std=[0.5])
+])
+
+# =========================
+# Decode
+# =========================
+def decode_prediction(logits, tokenizer):
+    pred_ids = logits.argmax(-1)[0]
+    chars = []
+    for t in pred_ids:
+        t = t.item()
+        if t == tokenizer.eos_id:
+            break
+        if t not in [tokenizer.pad_id, tokenizer.bos_id] and t < len(tokenizer._itos):
+            chars.append(tokenizer._itos[t])
+    return "".join(chars)
+
+# =========================
+# Model Cache
+# =========================
+model_cache = {}
+
+def load_model(model_path, lang_name):
+    cache_key = f"{lang_name}_{model_path}"
+    if cache_key in model_cache:
+        return model_cache[cache_key]
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    if not os.path.exists(model_path):
+        logger.error(f"Model not found: {model_path}")
+        return None, None, None
+
+    try:
+        # Load checkpoint with weights_only=False for compatibility with older models
+        checkpoint = torch.load(model_path, map_location='cpu', weights_only=False)
+
+        if 'charset' in checkpoint:
+            charset_str = checkpoint['charset']
+        elif lang_name == "Oriya":
+            charset_str = ORIYA_CHARSET
+        else:
+            # Try to infer charset from model
+            logger.warning(f"No charset found for {lang_name}, using default")
+            return None, None, None
+
+        model = torch.hub.load('baudm/parseq', 'parseq', pretrained=False, trust_repo=True)
+        model.tokenizer = Tokenizer(charset_str)
+
+        # Handle different checkpoint formats
+        if 'model_state_dict' in checkpoint:
+            state_dict = checkpoint['model_state_dict']
+        elif 'model' in checkpoint:
+            state_dict = checkpoint['model']
+        else:
+            state_dict = checkpoint
+        
+        # Remove unexpected keys if any
+        new_state_dict = {}
+        for k, v in state_dict.items():
+            if 'module.' in k:
+                k = k.replace('module.', '')
+            new_state_dict[k] = v
+        
+        model.load_state_dict(new_state_dict, strict=False)
+        model = model.to(device)
+        model.eval()
+
+        model_cache[cache_key] = (model, device, model.tokenizer)
+        logger.info(f"Loaded {lang_name} model successfully")
+        return model, device, model.tokenizer
+
+    except Exception as e:
+        logger.error(f"Error loading {lang_name}: {e}")
+        return None, None, None
+
+# =========================
+# Inference
+# =========================
+def inference_image(model, image, device, tokenizer):
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+
+    img_tensor = transform(image).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        logits = model(img_tensor)
+        predicted_text = decode_prediction(logits, tokenizer)
+
+        probs = torch.softmax(logits, dim=-1)
+        max_probs = probs.max(dim=-1)[0][0]
+        avg_conf = max_probs[:len(predicted_text)].mean().item() if len(predicted_text) > 0 else 0
+
+    return predicted_text, avg_conf
+
+# =========================
+# Get samples for specific language
+# =========================
+def get_samples_for_language(language):
+    """Get sample images for a specific language"""
+    config = LANGUAGES[language]
+    folder = config["samples_dir"]
+    samples = []
+    
+    if os.path.exists(folder):
+        for f in sorted(os.listdir(folder)):
+            if f.lower().endswith(('.png', '.jpg', '.jpeg')):
+                samples.append(os.path.join(folder, f))
+    
+    return samples[:6]
+
+# =========================
+# Create a tab for each language
+# =========================
+def create_language_tab(language):
+    """Create a tab interface for a specific language"""
+    
+    # Get samples for this language
+    sample_images = get_samples_for_language(language)
+    
+    with gr.Row():
+        # Left column - Image preview and controls
+        with gr.Column(scale=1):
+            image_input = gr.Image(
+                type="pil", 
+                label=f"📷 {language} Image Preview",
+                height=350,
+                interactive=True
+            )
+            
+            # Extract button right below the preview
+            extract_btn = gr.Button(
+                f"✨ Extract Text", 
+                variant="primary"
+            )
+            
+            # Sample images section
+            if sample_images:
+                gr.Markdown("---")
+                gr.Markdown(f"### 📸 Click any {language} sample image to preview")
+                
+                # Create gallery that doesn't expand when clicked
+                sample_gallery = gr.Gallery(
+                    value=sample_images,
+                    label=f"{language} Sample Images",
+                    columns=3,
+                    rows=2,
+                    object_fit="contain",
+                    height="auto",
+                    allow_preview=False,
+                    interactive=False
+                )
+                
+                # Function to update main preview when sample is selected
+                def update_preview_from_sample(evt: gr.SelectData):
+                    selected_index = evt.index
+                    selected_image_path = sample_images[selected_index]
+                    return Image.open(selected_image_path)
+                
+                sample_gallery.select(
+                    update_preview_from_sample,
+                    outputs=image_input
+                )
+        
+        # Right column - Results
+        with gr.Column(scale=1):
+            output_text = gr.Textbox(
+                label="📝 Extracted Text",
+                lines=6,
+                placeholder="Extracted text will appear here...",
+                interactive=False
+            )
+            confidence = gr.Textbox(
+                label="🎯 Confidence Score",
+                placeholder="Confidence will appear here...",
+                interactive=False
+            )
+    
+    # Handle prediction
+    def predict_wrapper(image):
+        if image is None:
+            return "⚠️ Please upload or select an image first", ""
+        
+        model_path = LANGUAGES[language]["model_path"]
+        model, device, tokenizer = load_model(model_path, language)
+        
+        if model is None:
+            return f"❌ Failed to load {language} model. Please check if the model file exists and is valid.", ""
+        
+        text, conf = inference_image(model, image, device, tokenizer)
+        
+        if text == "":
+            return "🔍 No text detected in the image", ""
+        
+        return text, f"✅ Confidence: {conf:.2%}"
+    
+    extract_btn.click(
+        fn=predict_wrapper,
+        inputs=[image_input],
+        outputs=[output_text, confidence]
+    )
+    
+    return image_input
+
+# =========================
+# Main UI with Tabs
+# =========================
+with gr.Blocks(theme=gr.themes.Soft(), title="Multilingual Scene Text Recognition", css="""
+    .gradio-container {
+        max-width: 1400px !important;
+        margin: auto !important;
+    }
+    
+    /* Make tab text clearly visible */
+    .tab-nav button {
+        font-size: 18px !important;
+        font-weight: bold !important;
+        padding: 12px 24px !important;
+        color: #000000 !important;
+        background-color: #f0f0f0 !important;
+        border: 2px solid #ccc !important;
+        margin-right: 8px !important;
+        border-radius: 8px 8px 0 0 !important;
+    }
+    
+    .tab-nav button.selected {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
+        color: white !important;
+        border: none !important;
+    }
+    
+    .tab-nav button:hover {
+        background-color: #e0e0e0 !important;
+        transform: translateY(-2px);
+    }
+    
+    /* Button styling */
+    button {
+        transition: all 0.3s ease !important;
+        font-weight: bold !important;
+        font-size: 16px !important;
+        margin-top: 10px !important;
+        margin-bottom: 10px !important;
+    }
+    
+    button:hover {
+        transform: translateY(-2px) !important;
+        box-shadow: 0 5px 15px rgba(0,0,0,0.2) !important;
+    }
+    
+    /* Gallery styling - prevent expansion */
+    .gr-gallery {
+        border: 2px solid #e0e0e0;
+        border-radius: 10px;
+        padding: 10px;
+        background-color: #fafafa;
+    }
+    
+    .gr-gallery .gallery-item {
+        cursor: pointer !important;
+        transition: transform 0.2s !important;
+    }
+    
+    .gr-gallery .gallery-item:hover {
+        transform: scale(1.05) !important;
+    }
+    
+    /* Box styling */
+    .gr-box {
+        border-radius: 10px;
+        border: 1px solid #e0e0e0;
+    }
+    
+    /* Primary button styling */
+    .gr-button-primary {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
+        color: white !important;
+        border: none !important;
+    }
+""") as demo:
+    
+    # Header
+    gr.Markdown("""
+    # 📖 Multilingual Scene Text Recognition System
+    ### Extract text from images in Telugu, Bengali, and Oriya languages
+    
+    ---
+    """)
+    
+    # Create tabs for each language
+    with gr.Tabs():
+        for lang in LANGUAGES.keys():
+            with gr.TabItem(f"🔤 {lang}"):
+                create_language_tab(lang)
+    
+    # Footer
+    gr.Markdown("""
+    ---
+    ### 💡 How to use:
+    1. **Select a language tab** (Telugu, Bengali, or Oriya)
+    2. **Click any sample thumbnail** - it will load into the main preview above
+    3. **Click "Extract Text"** button below the preview
+    4. **View results** on the right side
+    
+    ### 📌 Note:
+    - Sample thumbnails stay as thumbnails - they don't expand when clicked
+    - Only the main preview area changes when you click a sample
+    - You can also upload your own images
+    """)
+
+# =========================
+# Run
+# =========================
+if __name__ == "__main__":
+    # Check directories
+    for lang, config in LANGUAGES.items():
+        if not os.path.exists(config["model_path"]):
+            logger.warning(f"⚠️ Model not found: {config['model_path']} for {lang}")
+        if not os.path.exists(config["samples_dir"]):
+            os.makedirs(config["samples_dir"], exist_ok=True)
+            logger.warning(f"📁 Created samples directory: {config['samples_dir']}")
+    
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )