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| import os | |
| import json | |
| import gradio as gr | |
| from ultralytics import YOLO | |
| from PIL import Image, ImageDraw | |
| import torch | |
| from langchain_google_genai.chat_models import ChatGoogleGenerativeAI | |
| # Load model YOLOv8 | |
| model = YOLO("yolov8n.pt") | |
| # Load credentials (stringified JSON) from environment variable for Gemini | |
| credentials_string = os.environ.get("GOOGLE_APPLICATION_CREDENTIALS") | |
| if not credentials_string: | |
| raise ValueError("GOOGLE_APPLICATION_CREDENTIALS is not set in the environment!") | |
| # Parse the stringified JSON back to a Python dictionary | |
| credentials = json.loads(credentials_string) | |
| # Save the credentials to a temporary JSON file (required by Google SDKs) | |
| with open("service_account.json", "w") as f: | |
| json.dump(credentials, f) | |
| # Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the temporary file | |
| os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "service_account.json" | |
| # Initialize Gemini model (chatbot) | |
| llm = ChatGoogleGenerativeAI(model='gemini-1.5-pro') | |
| # Global chat history | |
| chat_history = [] | |
| # Fungsi untuk chatting dengan chatbot | |
| def chat_with_bot(message): | |
| global chat_history | |
| response = llm.predict(message) # Menggunakan Gemini untuk menghasilkan respon | |
| bot_response = f"Bot: {response}" | |
| chat_history.append((message, bot_response)) | |
| return chat_history | |
| # Fungsi untuk menganalisis gambar | |
| def analyze_image(image_path): | |
| global chat_history | |
| try: | |
| # Load gambar | |
| image = Image.open(image_path).convert("RGB") | |
| # Prediksi objek dalam gambar | |
| results = model(image) | |
| # Ambil hasil deteksi | |
| detected_objects = [] | |
| image_draw = image.copy() | |
| draw = ImageDraw.Draw(image_draw) | |
| for result in results: | |
| for box in result.boxes.data: | |
| x1, y1, x2, y2, score, class_id = box.tolist() | |
| if score > 0.5: # Hanya tampilkan objek dengan confidence score > 0.5 | |
| class_name = model.names[int(class_id)] | |
| detected_objects.append(f"{class_name} (score: {score:.2f})") | |
| draw.rectangle([x1, y1, x2, y2], outline="red", width=3) | |
| draw.text((x1, y1), class_name, fill="red") | |
| if detected_objects: | |
| bot_response = f"Objects detected: {', '.join(detected_objects)}." | |
| else: | |
| bot_response = "No objects detected." | |
| chat_history.append(("Uploaded an image for analysis", bot_response)) | |
| return image_draw, chat_history | |
| except Exception as e: | |
| error_msg = f"Error processing the image: {str(e)}" | |
| chat_history.append(("Error during image analysis", error_msg)) | |
| return None, chat_history | |
| # Bangun antarmuka Gradio | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Ken Chatbot") | |
| gr.Markdown("Ask me anything or upload an image for analysis!") | |
| chatbot = gr.Chatbot(elem_id="chatbot") | |
| msg = gr.Textbox(label="Type your message here...", placeholder="Enter your message...", show_label=False) | |
| send_btn = gr.Button("Send") | |
| img_upload = gr.Image(type="filepath", label="Upload an image for analysis") | |
| img_output = gr.Image(label="Detected Objects") | |
| msg.submit(chat_with_bot, msg, chatbot) | |
| send_btn.click(chat_with_bot, msg, chatbot) | |
| send_btn.click(lambda: "", None, msg) # Clear input field | |
| img_upload.change(analyze_image, img_upload, [img_output, chatbot]) | |
| demo.launch() | |