Delete app-dino.py

app-dino.py

@@ -1,158 +0,0 @@
-import gradio as gr
-from dotenv import load_dotenv
-from roboflow import Roboflow
-import tempfile
-import os
-import requests
-import cv2
-import numpy as np
-from dds_cloudapi_sdk import Config, Client
-from dds_cloudapi_sdk.tasks.dinox import DinoxTask
-from dds_cloudapi_sdk.tasks.types import DetectionTarget
-from dds_cloudapi_sdk import TextPrompt
-import supervision as sv
-
-# ========== Konfigurasi ==========
-load_dotenv()
-
-# Roboflow Config
-rf_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"))
-
-# DINO-X Config
-DINOX_API_KEY = os.getenv("DINO_X_API_KEY")
-DINOX_PROMPT = "beverage . food . drink . bottle"  # Customize sesuai produk kompetitor
-
-# Inisialisasi Model
-rf = Roboflow(api_key=rf_api_key)
-project = rf.workspace(workspace).project(project_name)
-yolo_model = project.version(model_version).model
-
-dinox_config = Config(DINOX_API_KEY)
-dinox_client = Client(dinox_config)
-
-# ========== Fungsi Deteksi Kombinasi ==========
-def detect_combined(image):
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-        image.save(temp_file, format="JPEG")
-        temp_path = temp_file.name
-    
-    try:
-        # ========== [1] YOLO: Deteksi Produk Nestlé (Per Class) ==========
-        yolo_pred = yolo_model.predict(temp_path, confidence=60, overlap=80).json()
-        
-        # Hitung per class Nestlé
-        nestle_class_count = {}
-        nestle_boxes = []
-        for pred in yolo_pred['predictions']:
-            class_name = pred['class']
-            nestle_class_count[class_name] = nestle_class_count.get(class_name, 0) + 1
-            nestle_boxes.append((pred['x'], pred['y'], pred['width'], pred['height']))
-
-        total_nestle = sum(nestle_class_count.values())
-        
-        # ========== [2] DINO-X: Deteksi Kompetitor ==========
-        image_url = dinox_client.upload_file(temp_path)
-        task = DinoxTask(
-            image_url=image_url,
-            prompts=[TextPrompt(text=DINOX_PROMPT)],
-            bbox_threshold=0.25,
-            targets=[DetectionTarget.BBox]
-        )
-        dinox_client.run_task(task)
-        dinox_pred = task.result.objects
-        
-        # Filter & Hitung Kompetitor
-        competitor_class_count = {}
-        competitor_boxes = []
-        for obj in dinox_pred:
-            dinox_box = obj.bbox
-            if not is_overlap(dinox_box, nestle_boxes):
-                class_name = obj.category.strip().lower()  # Normalisasi nama kelas
-                competitor_class_count[class_name] = competitor_class_count.get(class_name, 0) + 1
-                competitor_boxes.append({
-                    "class": class_name,
-                    "box": dinox_box,
-                    "confidence": obj.score
-                })
-        
-        total_competitor = sum(competitor_class_count.values())
-        
-        # ========== [3] Format Output ==========
-        result_text = "Product Nestle\n\n"
-        for class_name, count in nestle_class_count.items():
-            result_text += f"{class_name}: {count}\n"
-        result_text += f"\nTotal Product Nestle: {total_nestle}\n\n"
-        
-        result_text += "Competitor Products\n\n"
-        if competitor_class_count:
-            for class_name, count in competitor_class_count.items():
-                result_text += f"{class_name}: {count}\n"
-        else:
-            result_text += "No competitors detected\n"
-        result_text += f"\nTotal Competitor: {total_competitor}"
-        
-        # ========== [4] Visualisasi ==========
-        img = cv2.imread(temp_path)
-        
-        # Nestlé (Hijau)
-        for pred in yolo_pred['predictions']:
-            x, y, w, h = pred['x'], pred['y'], pred['width'], pred['height']
-            cv2.rectangle(img, (int(x-w/2), int(y-h/2)), (int(x+w/2), int(y+h/2)), (0,255,0), 2)
-            cv2.putText(img, pred['class'], (int(x-w/2), int(y-h/2-10)), 
-                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2)
-        
-        # Kompetitor (Merah)
-        for comp in competitor_boxes:
-            x1, y1, x2, y2 = comp['box']
-            cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0,0,255), 2)
-            cv2.putText(img, f"{comp['class']} {comp['confidence']:.2f}", 
-                       (int(x1), int(y1-10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 2)
-        
-        output_path = "/tmp/combined_output.jpg"
-        cv2.imwrite(output_path, img)
-        
-        return output_path, result_text
-    
-    except Exception as e:
-        return temp_path, f"Error: {str(e)}"
-    finally:
-        os.remove(temp_path)
-
-def is_overlap(box1, boxes2, threshold=0.3):
-    # Fungsi untuk deteksi overlap bounding box
-    x1_min, y1_min, x1_max, y1_max = box1
-    for b2 in boxes2:
-        x2, y2, w2, h2 = b2
-        x2_min = x2 - w2/2
-        x2_max = x2 + w2/2
-        y2_min = y2 - h2/2
-        y2_max = y2 + h2/2
-        
-        # Hitung area overlap
-        dx = min(x1_max, x2_max) - max(x1_min, x2_min)
-        dy = min(y1_max, y2_max) - max(y1_min, y2_min)
-        if (dx >= 0) and (dy >= 0):
-            area_overlap = dx * dy
-            area_box1 = (x1_max - x1_min) * (y1_max - y1_min)
-            if area_overlap / area_box1 > threshold:
-                return True
-    return False
-
-# ========== Gradio Interface ==========
-with gr.Blocks() as iface:
-    with gr.Row():
-        input_image = gr.Image(type="pil", label="Input Image")
-        output_image = gr.Image(label="Detection Result")
-        output_text = gr.Textbox(label="Product Counts")
-    
-    detect_button = gr.Button("Detect Products")
-    detect_button.click(
-        fn=detect_combined,
-        inputs=input_image,
-        outputs=[output_image, output_text]
-    )
-
-iface.launch()