Update app.py

app.py

@@ -1,108 +1,72 @@
 import gradio as gr
+import supervision as sv
+import numpy as np
+import cv2
+from inference import get_roboflow_model
 from dotenv import load_dotenv
-from roboflow import Roboflow
-import tempfile
 import os
-import requests
-from PIL import Image
 
-# Muat variabel lingkungan dari file .env
+# Load environment variables from .env file
 load_dotenv()
 api_key = os.getenv("ROBOFLOW_API_KEY")
-workspace = os.getenv("ROBOFLOW_WORKSPACE")
-project_name = os.getenv("ROBOFLOW_PROJECT")
-model_version = int(os.getenv("ROBOFLOW_MODEL_VERSION"))
-
-# Inisialisasi Roboflow menggunakan data yang diambil dari secrets
-rf = Roboflow(api_key=api_key)
-project = rf.workspace(workspace).project(project_name)
-model = project.version(model_version).model
-
-# Fungsi untuk memotong gambar menjadi potongan-potongan kecil
-def slice_image(image, slice_size=512, overlap=0):
-    width, height = image.size
-    slices = []
-
-    step = slice_size - overlap
-
-    for top in range(0, height, step):
-        for left in range(0, width, step):
-            bottom = min(top + slice_size, height)
-            right = min(left + slice_size, width)
-            slices.append((left, top, right, bottom))
-
-    return slices
-
-# Fungsi untuk menangani input dan output gambar
-def detect_objects(image):
-    slice_size = 512
-    overlap = 50
-
-    # Potong gambar menjadi bagian kecil
-    slices = slice_image(image, slice_size, overlap)
-    results = []
-    class_count = {}
-    total_count = 0
-
-    for i, (left, top, right, bottom) in enumerate(slices):
-        sliced_image = image.crop((left, top, right, bottom))
-
-        # Simpan gambar slice sementara
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-            sliced_image.save(temp_file, format="JPEG")
-            temp_file_path = temp_file.name
-
-        try:
-            # Lakukan prediksi pada setiap slice
-            predictions = model.predict(temp_file_path, confidence=60, overlap=80).json()
-
-            for prediction in predictions['predictions']:
-                prediction["left"] += left
-                prediction["top"] += top
-                prediction["right"] += left
-                prediction["bottom"] += top
-
-                results.append(prediction)
-
-                # Perbarui jumlah objek per kelas
-                class_name = prediction['class']
-                class_count[class_name] = class_count.get(class_name, 0) + 1
-                total_count += 1
-        except requests.exceptions.HTTPError as http_err:
-            return f"HTTP error occurred: {http_err}", None
-        except Exception as err:
-            return f"An error occurred: {err}", None
-        finally:
-            os.remove(temp_file_path)
-
-    # Gabungkan hasil deteksi
-    result_text = "Product Nestle\n\n"
-    for class_name, count in class_count.items():
+model_id = os.getenv("ROBOFLOW_MODEL_ID")  # Example: "people-detection-general"
+model_version = os.getenv("ROBOFLOW_MODEL_VERSION")  # Example: "5"
+
+# Initialize the Roboflow model
+model = get_roboflow_model(model_id=f"{model_id}/{model_version}", api_key=api_key)
+
+# Callback function for SAHI Slicer
+def callback(image_slice: np.ndarray) -> sv.Detections:
+    results = model.infer(image_slice)[0]
+    return sv.Detections.from_inference(results)
+
+# Object detection function
+def detect_objects_with_sahi(image):
+    # Convert Gradio PIL image to NumPy array
+    image_np = np.array(image)
+
+    # Run inference with SAHI Slicer
+    slicer = sv.InferenceSlicer(callback=callback)
+    sliced_detections = slicer(image=image_np)
+
+    # Annotate image with detected objects
+    label_annotator = sv.LabelAnnotator()
+    box_annotator = sv.BoxAnnotator()
+    annotated_image = box_annotator.annotate(scene=image_np.copy(), detections=sliced_detections)
+    annotated_image = label_annotator.annotate(scene=annotated_image, detections=sliced_detections)
+
+    # Count objects by class
+    class_counts = {}
+    for detection in sliced_detections:
+        class_name = detection.class_id
+        class_counts[class_name] = class_counts.get(class_name, 0) + 1
+
+    # Create summary text
+    total_objects = sum(class_counts.values())
+    result_text = "Detected Objects:\n"
+    for class_name, count in class_counts.items():
         result_text += f"{class_name}: {count}\n"
-    result_text += f"\nTotal Product Nestle: {total_count}"
+    result_text += f"\nTotal Objects: {total_objects}"
 
-    # Kembalikan hasil
-    return image, result_text
+    # Return the annotated image and summary text
+    return annotated_image, result_text
 
-# Membuat antarmuka Gradio dengan tata letak fleksibel
-with gr.Blocks() as iface:
+# Create Gradio interface
+with gr.Blocks() as app:
     with gr.Row():
         with gr.Column():
-            input_image = gr.Image(type="pil", label="Input Image")
+            input_image = gr.Image(type="pil", label="Upload Image")
+            detect_button = gr.Button("Detect Objects")
         with gr.Column():
-            output_image = gr.Image(label="Detect Object")
-        with gr.Column():
-            output_text = gr.Textbox(label="Counting Object")
-
-    # Tombol untuk memproses input
-    detect_button = gr.Button("Detect")
+            output_image = gr.Image(label="Annotated Image")
+            output_text = gr.Textbox(label="Object Count Summary", lines=10)
 
-    # Hubungkan tombol dengan fungsi deteksi
+    # Link button to detection function
     detect_button.click(
-        fn=detect_objects,
+        fn=detect_objects_with_sahi,
         inputs=input_image,
         outputs=[output_image, output_text]
     )
 
-# Menjalankan antarmuka
-iface.launch()
+# Launch Gradio app
+app.launch()