you done all well but the gemini output not got.....here is my other project where i used gemini to detect bottle lable .....see its implementation and use its useful part...remember here we are not detecting object we directly send image to gemini at every 10 sends ......import gradio as gr import cv2 import numpy as np import os import time import threading import base64 from ultralytics import YOLO from langchain_core.messages import HumanMessage from langchain_google_genai import ChatGoogleGenerativeAI # Set up Google API Key os.environ["GOOGLE_API_KEY"] = "YOUR_GOOGLE_API_KEY" # Replace with your API Key gemini_model = ChatGoogleGenerativeAI(model="gemini-1.5-flash") # Load YOLO model yolo_model = YOLO("best.pt") names = yolo_model.names # Constants for ROI detection cx1 = 491 offset = 8 current_date = time.strftime("%Y-%m-%d") crop_folder = f"crop_{current_date}" if not os.path.exists(crop_folder): os.makedirs(crop_folder) # Track processed IDs to avoid duplicate processing processed_track_ids = set() lock = threading.Lock() # Ensure thread-safe operations def encode_image_to_base64(image): _, img_buffer = cv2.imencode('.jpg', image) return base64.b64encode(img_buffer).decode('utf-8') def analyze_image_with_gemini(current_image): if current_image is None: return "No image available for analysis." current_image_data = encode_image_to_base64(current_image) message = HumanMessage( content=[ {"type": "text", "text": "Analyze this image and check if the label is present on the bottle. Return results in a structured format."}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{current_image_data}"}, "description": "Detected product"} ] ) try: response = gemini_model.invoke([message]) return response.content except Exception as e: return f"Error processing image: {e}" def save_crop_image(crop, track_id): filename = f"{crop_folder}/{track_id}.jpg" cv2.imwrite(filename, crop) return filename def process_crop_image(crop, track_id, responses): response = analyze_image_with_gemini(crop) responses.append((track_id, response)) def process_video(video_path): cap = cv2.VideoCapture(video_path) output_path = "output_video.mp4" fourcc = cv2.VideoWriter_fourcc(*"mp4v") out = cv2.VideoWriter(output_path, fourcc, 20.0, (1020, 500)) responses = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break frame = cv2.resize(frame, (1020, 500)) results = yolo_model.track(frame, persist=True) if results[0].boxes is not None: boxes = results[0].boxes.xyxy.int().cpu().tolist() track_ids = results[0].boxes.id.int().cpu().tolist() if results[0].boxes.id is not None else [-1] * len(boxes) for box, track_id in zip(boxes, track_ids): with lock: # Prevent race condition if track_id not in processed_track_ids: x1, y1, x2, y2 = box crop = frame[y1:y2, x1:x2] save_crop_image(crop, track_id) threading.Thread(target=process_crop_image, args=(crop, track_id, responses)).start() processed_track_ids.add(track_id) out.write(frame) cap.release() out.release() return output_path, responses def process_and_return(video_file): if not video_file: return None, "No video uploaded." video_path = "uploaded_video.mp4" with open(video_path, "wb") as f: f.write(video_file) output_video_path, analysis_results = process_video(video_path) results_text = "\n".join([f"**Track ID {track_id}:** {response}" for track_id, response in analysis_results]) return output_video_path, results_text # Gradio Interface with gr.Blocks() as demo: gr.Markdown("# Bottle Label Checking using YOLO & Gemini AI") with gr.Row(): video_input = gr.File(label="Upload a video", type="binary") process_button = gr.Button("Process Video") with gr.Row(): video_output = gr.Video(label="Processed Video") download_button = gr.File(label="Download Processed Video") analysis_results = gr.Markdown(label="AI Analysis Results") process_button.click( fn=process_and_return, inputs=video_input, outputs=[video_output, analysis_results] ) download_button.change( fn=lambda x: x if x else None, inputs=video_output, outputs=download_button ) demo.launch() - Follow Up Deployment