Create app.py

app.py

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+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
+import subprocess
+
+# ========== 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"))
+
+# Landing AI CountGD Config
+COUNTGD_API_KEY = os.getenv("COUNTGD_API_KEY")
+COUNTGD_URL = "https://api.landing.ai/v1/tools/text-to-object-detection"
+COUNTGD_PROMPT = ["beverage", "bottle", "cans", "mixed box"]  # Sesuaikan prompt
+
+# Inisialisasi Model
+rf = Roboflow(api_key=rf_api_key)
+project = rf.workspace(workspace).project(project_name)
+yolo_model = project.version(model_version).model
+
+# ========== 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 ========== 
+        yolo_pred = yolo_model.predict(temp_path, confidence=50, overlap=80).json()
+        
+        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] CountGD: Deteksi Kompetitor ========== 
+        with open(temp_path, 'rb') as img_file:
+            files = {"image": img_file}
+            data = {"prompts": COUNTGD_PROMPT, "model": "countgd"}
+            headers = {"Authorization": f"Basic {COUNTGD_API_KEY}"}
+            response = requests.post(COUNTGD_URL, files=files, data=data, headers=headers)
+            countgd_pred = response.json()
+        
+        competitor_class_count = {}
+        competitor_boxes = []
+        for obj in countgd_pred.get("objects", []):
+            class_name = obj['category'].strip().lower()
+            competitor_class_count[class_name] = competitor_class_count.get(class_name, 0) + 1
+            competitor_boxes.append({
+                "class": class_name,
+                "box": obj['bbox'],
+                "confidence": obj['score']
+            })
+        
+        total_competitor = sum(competitor_class_count.values())
+        
+        # ========== [3] Format Output ========== 
+        result_text = "Product Nestlé\n\n"
+        for class_name, count in nestle_class_count.items():
+            result_text += f"{class_name}: {count}\n"
+        result_text += f"\nTotal Products Nestlé: {total_nestle}\n\n"
+        
+        if competitor_class_count:
+            result_text += f"Total Unclassified Products: {total_competitor}\n"
+        else:
+            result_text += "No Unclassified Products detected\n"
+        
+        # ========== [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)
+
+# ========== Gradio Interface ========== 
+with gr.Blocks(theme=gr.themes.Base(primary_hue="teal", secondary_hue="teal", neutral_hue="slate")) as iface:
+    gr.Markdown("""<div style="text-align: center;"><h1>NESTLE - STOCK COUNTING</h1></div>""")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label="Input Image")
+            detect_image_button = gr.Button("Detect Image")
+            output_image = gr.Image(label="Detect Object")
+            output_text = gr.Textbox(label="Counting Object")
+            detect_image_button.click(fn=detect_combined, inputs=input_image, outputs=[output_image, output_text])
+
+iface.launch()