Delete app.py

app.py

@@ -1,169 +0,0 @@
-import gradio as gr
-from roboflow import Roboflow
-import tempfile
-import os
-from sahi.slicing import slice_image
-import numpy as np
-import cv2
-from PIL import Image, ImageDraw
-
-# Initialize Roboflow
-rf = Roboflow(api_key="Otg64Ra6wNOgDyjuhMYU")
-project = rf.workspace("alat-pelindung-diri").project("nescafe-4base")
-model = project.version(20).model
-
-def apply_nms(predictions, iou_threshold=0.5):
-    boxes = []
-    scores = []
-    classes = []
-
-    # Extract boxes, scores, and class info
-    for prediction in predictions:
-        # Construct the bounding box from x, y, width, height
-        x = prediction['x']
-        y = prediction['y']
-        width = prediction['width']
-        height = prediction['height']
-        box = [x, y, width, height]
-
-        boxes.append(box)
-        scores.append(prediction['confidence'])
-        classes.append(prediction['class'])
-
-    boxes = np.array(boxes)
-    scores = np.array(scores)
-    classes = np.array(classes)
-
-    # Perform NMS using OpenCV
-    indices = cv2.dnn.NMSBoxes(boxes.tolist(), scores.tolist(), score_threshold=0.25, nms_threshold=iou_threshold)
-
-    print(f"Predictions before NMS: {predictions}")
-    print(f"Indices after NMS: {indices}")
-
-    # Check if indices is empty or invalid
-    if indices is None or len(indices) == 0:
-        print("No valid indices returned from NMS.")
-        return []  # Return an empty list if no valid indices are found
-
-    # Flatten indices array (if returned as a tuple)
-    indices = indices.flatten()
-
-    nms_predictions = []
-
-    for i in indices:
-        nms_predictions.append({
-            'class': classes[i],
-            'bbox': boxes[i],  # Now using the constructed box
-            'confidence': scores[i]
-        })
-
-    return nms_predictions
-
-# Detect objects and annotate the image
-def detect_objects(image):
-    # Save the image temporarily
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-        image.save(temp_file, format="JPEG")
-        temp_file_path = temp_file.name
-
-    # Slice the image into smaller pieces
-    slice_image_result = slice_image(
-        image=temp_file_path,
-        output_file_name="sliced_image",
-        output_dir="/tmp/sliced/",
-        slice_height=256,
-        slice_width=256,
-        overlap_height_ratio=0.1,
-        overlap_width_ratio=0.1
-    )
-
-    # Print to check the available attributes of the slice_image_result object
-    print(f"Slice result: {slice_image_result}")
-
-    # Try accessing the sliced image paths from the result object
-    try:
-        sliced_image_paths = slice_image_result.sliced_image_paths  # Assuming this is the correct attribute
-        print(f"Sliced image paths: {sliced_image_paths}")
-    except AttributeError:
-        print("Failed to access sliced_image_paths attribute.")
-        sliced_image_paths = []
-
-    # Check predictions for the whole image first
-    print("Predicting on the whole image (without slicing)...")
-    whole_image_predictions = model.predict(image_path=temp_file_path).json()
-    print(f"Whole image predictions: {whole_image_predictions}")
-
-    # If there are predictions, return them
-    if whole_image_predictions['predictions']:
-        print("Using predictions from the whole image.")
-        all_predictions = whole_image_predictions['predictions']
-    else:
-        print("No predictions found for the whole image. Predicting on slices...")
-        # If no predictions for the whole image, predict on slices
-        all_predictions = []
-
-        for sliced_image_path in sliced_image_paths:
-            if isinstance(sliced_image_path, str):
-                predictions = model.predict(image_path=sliced_image_path).json()
-                all_predictions.extend(predictions['predictions'])
-            else:
-                print(f"Skipping invalid image path: {sliced_image_path}")
-
-    # Apply NMS to remove duplicate detections
-    postprocessed_predictions = apply_nms(all_predictions, iou_threshold=0.5)
-
-    # Annotate the image with prediction results using OpenCV
-    img = cv2.imread(temp_file_path)
-    for prediction in postprocessed_predictions:
-        class_name = prediction['class']
-        bbox = prediction['bbox']
-        confidence = prediction['confidence']
-        
-        # Unpack the bounding box coordinates
-        x, y, w, h = map(int, bbox)
-
-        # Draw the bounding box and label on the image
-        color = (0, 255, 0)  # Green color for the box
-        thickness = 2
-        cv2.rectangle(img, (x, y), (x + w, y + h), color, thickness)
-
-        label = f"{class_name}: {confidence:.2f}"
-        cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, thickness)
-
-    # Convert the image from BGR to RGB for PIL compatibility
-    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    annotated_image = Image.fromarray(img_rgb)
-
-    # Save the annotated image
-    output_image_path = "/tmp/prediction.jpg"
-    annotated_image.save(output_image_path)
-
-    # Count objects per class
-    class_count = {}
-    for detection in postprocessed_predictions:
-        class_name = detection['class']
-        if class_name in class_count:
-            class_count[class_name] += 1
-        else:
-            class_count[class_name] = 1
-
-    # Object count result
-    result_text = "Jumlah objek per kelas:\n"
-    for class_name, count in class_count.items():
-        result_text += f"{class_name}: {count} objek\n"
-
-    # Remove temporary file
-    os.remove(temp_file_path)
-
-    return output_image_path, result_text
-
-# Gradio interface
-iface = gr.Interface(
-    fn=detect_objects,                         # Function called when image is uploaded
-    inputs=gr.Image(type="pil"),               # Input is an image
-    outputs=[gr.Image(), gr.Textbox()],        # Output is an image and text
-    live=True                                    # Display results live
-)
-
-# Run the interface
-iface.launch()