Create app.py

app.py

@@ -0,0 +1,285 @@
+import os
+import json
+import pandas as pd
+import gradio as gr
+import numpy as np
+from PIL import Image
+from theme_tops import DarkTheme
+from clip_base import OpenAiClipModel
+import tensorflow as tf
+tagged_images = {}
+
+MODEL_PATH = os.path.join(os.getcwd(), 'clip_tflite_model.tflite')
+JSON_PATH = os.path.join(os.getcwd(), 'categories.json')
+
+def test_model(image):
+    """Test the TFLite model with an uploaded image"""
+    try:
+        # Check if model and JSON files exist
+        if not os.path.exists(MODEL_PATH):
+            return "Error: Model file not found. Please generate the model first."
+        if not os.path.exists(JSON_PATH):
+            return "Error: Categories file not found. Please generate the model first."
+
+        # Load and preprocess image
+        processed_image = load_and_preprocess_image(image)
+
+        # Load the TFLite model
+        interpreter = tf.lite.Interpreter(model_path=MODEL_PATH)
+        interpreter.allocate_tensors()
+
+        # Get input and output details
+        input_details = interpreter.get_input_details()
+        output_details = interpreter.get_output_details()
+
+        interpreter.set_tensor(input_details[0]['index'], processed_image)
+
+        interpreter.invoke()
+
+        embeddings = interpreter.get_tensor(output_details[0]['index'])
+
+        with open(JSON_PATH, 'r') as f:
+            categories = json.load(f)
+
+        scores_with_ids = []
+        for i, score in enumerate(embeddings.flatten()):
+            scores_with_ids.append((float(score), i))
+
+        scores_with_ids.sort(reverse=True)  # Sort by score (first element of tuple)
+
+        top_results = scores_with_ids[:5]
+
+        results = []
+        for score, category_id in top_results:
+            percentage = score * 100
+            category = next((cat['title'] for cat in categories if cat['id'] == category_id),
+                            f"Category {category_id}")
+            results.append(f"{category}: {percentage:.2f}%")
+
+        return "\n".join(results)
+
+    except Exception as e:
+        return f"Error processing image: {str(e)}"
+
+def load_and_preprocess_image(image):
+    """Preprocess image for model input"""
+    if isinstance(image, str):
+        image = Image.open(image)
+    elif isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+
+    image = image.resize((224, 224))
+    image = image.convert('RGB')
+    image = np.array(image).astype(np.float32) / 255.0
+    image = np.expand_dims(image, axis=0)
+    return image
+
+def process_images(payload):
+    tflite_model = OpenAiClipModel(payload=payload).build_model()
+
+    return tflite_model
+
+# Function to add a new tag category
+def add_tag_category(tag_category):
+    # Normalize and validate tag category
+    tag_category = tag_category.strip()
+    if not tag_category:
+        return "Please enter a valid tag category", None
+
+    # Initialize the tag category if it doesn't exist
+    if tag_category not in tagged_images:
+        tagged_images[tag_category] = []
+
+    return f"Tag Category '{tag_category}' Added", gr.File(visible=True), ""
+
+
+# Function to get updated tag category choices
+def get_tag_category_choices():
+    return gr.Dropdown(choices=list(tagged_images.keys()))
+
+
+def show_category_images(tag_category):
+    if not tag_category:
+        return None, None
+
+    if tag_category in tagged_images:
+        return (
+            gr.Gallery(value=tagged_images[tag_category]),
+            tagged_images[tag_category]
+        )
+    return None, None
+
+
+# Function to upload images for a specific tag category
+def upload_images_for_tag(tag_category, image_files):
+    # Ensure the tag category exists
+    if tag_category not in tagged_images:
+        return "Tag category not found. Add the tag category first.", None, None
+
+    # Replace existing images with new ones for the tag category
+    tagged_images[tag_category] = [file.name for file in image_files]  # Replace instead of append
+
+    return (
+        f"Added {len(image_files)} images to '{tag_category}'",
+        gr.Gallery(value=[file.name for file in image_files]),
+        tagged_images
+    )
+
+
+
+# Function to export tagged images
+def export_tagged_images():
+    return tagged_images
+
+def clear_uploaded_images():
+    return None, None
+
+# Gradio UI
+with gr.Blocks(theme=DarkTheme()) as demo:
+    gr.Markdown("# Clip -> Tflite - TOPS Infosolutions Pvt Ltd")
+    gr.Markdown("Add Classification Tags")
+
+    # Tag Category Input
+    with gr.Row():
+        tag_category_input = gr.Textbox(
+            label="Enter Tag Category",
+            placeholder="e.g., Smartphone, Laptop, Tablet"
+        )
+        # add_tag_category_btn = gr.Button("Add Tag Category")
+        tag_category_status = gr.Textbox(label="Action Status", interactive=False)
+
+    gr.Markdown("Images")
+
+    # Image Upload for Specific Tag
+    with gr.Row():
+        tag_category_selector = gr.Dropdown(label="Select Tag Category", choices=[])
+        image_upload = gr.File(
+            file_types=["image"],
+            file_count="multiple",
+            label="Upload Images",
+            visible=False
+        )
+        upload_images_btn = gr.Button("Upload Images for Category")
+        clear_upload_btn = gr.Button("Clear Upload")
+
+    # Image Gallery with smaller previews
+    image_gallery = gr.Gallery(
+        label="Uploaded Images",
+        columns=[6],  # Show 4 images per row
+        rows=[1],  # Show 2 rows
+        height="20",
+        object_fit="contain",  # Maintain aspect ratio
+        preview=False,
+        show_label=False,
+        elem_classes="small-gallery"  # Custom CSS class for additional styling
+    )
+
+    # Export Section
+    with gr.Row():
+        # export_btn = gr.Button("Export Tagged Images")
+        export_output = gr.JSON(label="Exported Tagged Images")
+
+    with gr.Row():
+        submit_btn = gr.Button("Process Images")
+
+
+    with gr.Row():
+        download_button_tflite = gr.File(
+            label="Download Tflite Model",
+            file_count="single",
+            interactive=False,
+            type="filepath"
+        )
+
+    with gr.Tab("Test Model"):
+        with gr.Row():
+            with gr.Column():
+                test_image = gr.Image(
+                    label="Upload Image to Test",
+                    type="numpy"
+                )
+                test_button = gr.Button("Test Image")
+
+            with gr.Column():
+                output_text = gr.Textbox(
+                    label="Prediction Results",
+                    lines=6,
+                    interactive=False
+                )
+
+        test_button.click(
+            fn=test_model,
+            inputs=[test_image],
+            outputs=[output_text]
+        )
+
+
+    submit_btn.click(
+        fn=process_images,
+        inputs=[export_output],
+        outputs=[download_button_tflite]
+    )
+
+    # Add custom CSS for smaller gallery images
+    demo.load(js="""
+        function() {
+            const style = document.createElement('style');
+            style.textContent = `         
+                .small-gallery img {
+                    max-height: 150px !important;
+                    width: auto !important;
+                    object-fit: contain !important;
+                }
+                .small-gallery .grid-container {
+                    gap: 10px !important;
+                }
+            `;
+            document.head.appendChild(style);
+        }
+    """)
+
+    # Functionality Connections
+    # Add both button click and Enter key press handlers
+    # add_tag_category_btn.click(
+    #     add_tag_category,
+    #     tag_category_input,
+    #     [tag_category_status, image_upload, tag_category_input]
+    # ).then(
+    #     get_tag_category_choices,
+    #     None,
+    #     tag_category_selector
+    # )
+
+    # Add Enter key press handler
+    tag_category_input.submit(
+        add_tag_category,
+        tag_category_input,
+        [tag_category_status, image_upload, tag_category_input]
+    ).then(
+        get_tag_category_choices,
+        None,
+        tag_category_selector
+    )
+
+    tag_category_selector.change(
+        show_category_images,
+        tag_category_selector,
+        [image_gallery, image_upload]
+    )
+
+    upload_images_btn.click(
+        upload_images_for_tag,
+        [tag_category_selector, image_upload],
+        [tag_category_status, image_gallery, export_output]
+    )
+
+    clear_upload_btn.click(
+        clear_uploaded_images,
+        [],
+        [image_upload, image_gallery]
+    )
+
+    # export_btn.click(export_tagged_images, None, export_output)
+
+# Launch the app
+demo.launch()