Delete app-video.py

app-video.py

@@ -1,142 +0,0 @@
-import gradio as gr
-from dotenv import load_dotenv
-from roboflow import Roboflow
-import tempfile
-import os
-import requests
-import cv2
-
-# Muat variabel lingkungan dari file .env
-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 menangani deteksi pada gambar
-def detect_objects(image):
-    # Simpan gambar yang diupload sebagai file sementara
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-        image.save(temp_file, format="JPEG")
-        temp_file_path = temp_file.name
-
-    try:
-        # Lakukan prediksi pada gambar
-        predictions = model.predict(temp_file_path, confidence=60, overlap=80).json()
-
-        # Menghitung jumlah objek per kelas
-        class_count = {}
-        total_count = 0
-
-        for prediction in predictions['predictions']:
-            class_name = prediction['class']
-            class_count[class_name] = class_count.get(class_name, 0) + 1
-            total_count += 1
-
-        # Menyusun output berupa string hasil perhitungan
-        result_text = "Product Nestle\n\n"
-        for class_name, count in class_count.items():
-            result_text += f"{class_name}: {count}\n"
-        result_text += f"\nTotal Product Nestle: {total_count}"
-
-        # Menyimpan gambar dengan prediksi
-        output_image_path = "/tmp/prediction.jpg"
-        model.predict(temp_file_path, confidence=60, overlap=80).save(output_image_path)
-        
-    except requests.exceptions.HTTPError as http_err:
-        result_text = f"HTTP error occurred: {http_err}"
-        output_image_path = temp_file_path
-    except Exception as err:
-        result_text = f"An error occurred: {err}"
-        output_image_path = temp_file_path
-
-    os.remove(temp_file_path)
-    
-    return output_image_path, result_text
-
-# Fungsi untuk menangani deteksi pada video
-def detect_objects_in_video(video_path):
-    temp_output_path = "/tmp/output_video.mp4"
-    temp_frames_dir = tempfile.mkdtemp()
-
-    try:
-        # Baca video dan ekstrak frame
-        video = cv2.VideoCapture(video_path)
-        frame_rate = int(video.get(cv2.CAP_PROP_FPS))
-        frame_width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
-        frame_height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        frame_size = (frame_width, frame_height)
-        frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
-
-        # VideoWriter untuk membuat video keluaran
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-        output_video = cv2.VideoWriter(temp_output_path, fourcc, frame_rate, frame_size)
-
-        frame_index = 0
-        while True:
-            ret, frame = video.read()
-            if not ret:
-                break
-
-            # Simpan frame sementara untuk prediksi
-            frame_path = os.path.join(temp_frames_dir, f"frame_{frame_index}.jpg")
-            cv2.imwrite(frame_path, frame)
-
-            # Deteksi objek pada frame
-            predictions = model.predict(frame_path, confidence=60, overlap=80).json()
-
-            # Gambar bounding box pada frame
-            for prediction in predictions['predictions']:
-                x, y, w, h = prediction['x'], prediction['y'], prediction['width'], prediction['height']
-                class_name = prediction['class']
-                color = (0, 255, 0)  # Hijau
-                cv2.rectangle(frame, (int(x - w/2), int(y - h/2)), (int(x + w/2), int(y + h/2)), color, 2)
-                cv2.putText(frame, class_name, (int(x - w/2), int(y - h/2 - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
-            # Tambahkan frame ke video keluaran
-            output_video.write(frame)
-            frame_index += 1
-
-        video.release()
-        output_video.release()
-
-        return temp_output_path, "Video processing completed successfully."
-    
-    except Exception as e:
-        return None, f"An error occurred: {e}"
-
-# Membuat antarmuka Gradio dengan tata letak fleksibel
-with gr.Blocks() as iface:
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(type="pil", label="Input Image")
-            input_video = gr.Video(label="Input Video")  # Updated line
-        with gr.Column():
-            output_image = gr.Image(label="Detect Object")
-            output_video = gr.Video(label="Output Video")
-        with gr.Column():
-            output_text = gr.Textbox(label="Counting Object")
-    
-    # Tombol untuk memproses gambar
-    detect_image_button = gr.Button("Detect Image")
-    detect_image_button.click(
-        fn=detect_objects, 
-        inputs=input_image, 
-        outputs=[output_image, output_text]
-    )
-
-    # Tombol untuk memproses video
-    detect_video_button = gr.Button("Detect Video")
-    detect_video_button.click(
-        fn=detect_objects_in_video, 
-        inputs=input_video, 
-        outputs=[output_video, output_text]
-    )
-
-# Menjalankan antarmuka
-iface.launch()