app.py

import os
import gradio as gr
from huggingface_hub import snapshot_download
import tensorflow as tf
from PIL import Image
import numpy as np
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils

# Path to the label map
PATH_TO_LABELS = 'data/label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

def pil_image_as_numpy_array(pilimg):
    img_array = tf.keras.utils.img_to_array(pilimg)
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

def load_model(repo_id):
    download_dir = snapshot_download(repo_id)
    saved_model_dir = os.path.join(download_dir, "saved_model")
    detection_model = tf.saved_model.load(saved_model_dir)
    return detection_model

# List of sample images in 'test_samples' folder
sample_images = [file for file in os.listdir('test_samples') if file.endswith(('.png', '.jpg', '.jpeg'))]
sample_images.sort()  # Optional: sort the file names

def predict(uploaded_img, use_sample, sample_choice):
    if use_sample:
        # Use the selected sample image
        pilimg = Image.open(os.path.join('test_samples', sample_choice))
    else:
        # Use the uploaded image
        pilimg = uploaded_img

    image_np = pil_image_as_numpy_array(pilimg)
    return predict_combined_models(image_np, detection_model_fin, detection_model_ini)


def predict_combined_models(image_np, model1, model2):
    # Process with first model
    results1 = model1(image_np)
    result1 = {key:value.numpy() for key,value in results1.items()}

    # Process with second model
    results2 = model2(image_np)
    result2 = {key:value.numpy() for key,value in results2.items()}

    # Modify category index for each model
    category_index_model1 = {k: {**v, 'name': v['name'] + ' - Model 1'} for k, v in category_index.items()}
    category_index_model2 = {k: {**v, 'name': v['name'] + ' - Model 2'} for k, v in category_index.items()}

    # Visualization for model 1
    image_np_with_detections = image_np.copy()
    viz_utils.visualize_boxes_and_labels_on_image_array(
        image_np_with_detections[0],
        result1['detection_boxes'][0],
        (result1['detection_classes'][0]).astype(int),
        result1['detection_scores'][0],
        category_index_model1,
        use_normalized_coordinates=True,
        max_boxes_to_draw=200,
        min_score_thresh=.60,
        agnostic_mode=False,
        line_thickness=2)

    # Visualization for model 2
    viz_utils.visualize_boxes_and_labels_on_image_array(
        image_np_with_detections[0],
        result2['detection_boxes'][0],
        (result2['detection_classes'][0]).astype(int),
        result2['detection_scores'][0],
        category_index_model2,
        use_normalized_coordinates=True,
        max_boxes_to_draw=200,
        min_score_thresh=.60,
        agnostic_mode=False,
        line_thickness=2)

    # Combine and return final image
    result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
    return result_pil_img

# Load your models
REPO_ID1 = "dtyago/23a052w-iti107-assn2_tfodmodel"
REPO_ID2 = "dtyago/23a052w-iti107-assn2_tfodmodel_run1"
detection_model_fin = load_model(REPO_ID1)
detection_model_ini = load_model(REPO_ID2)

# Gradio interface
gr.Interface(
    fn=predict, 
    inputs=[
        gr.Image(type="pil"),  # Image upload
        gr.Checkbox(label="Use a sample image instead"),  # Checkbox to choose a sample
        gr.Dropdown(choices=sample_images, label="Select a Sample Image")  # Dropdown to select a sample
    ], 
    outputs=gr.Image(type="pil"),
    title="Gramophone & Veena Detection with TensorFlow Models",
    description="This app uses TensorFlow models to detect objects in images. Upload an image to see the detected gramophone and veena. Bounding boxes labeled Run1 = - Model 2, Run2= - Model 1"
).launch(share=True)