import cv2 import gradio as gr import google.generativeai as genai import os import PIL.Image # Configure the API key for Google Generative AI genai.configure(api_key=os.environ.get("GOOGLE_API_KEY")) # Define the Generative AI model model = genai.GenerativeModel('gemini-1.5-flash') # Function to capture frames from a video def frame_capture(video_path, num_frames=5): vidObj = cv2.VideoCapture(video_path) frames = [] total_frames = int(vidObj.get(cv2.CAP_PROP_FRAME_COUNT)) frame_step = max(1, total_frames // num_frames) count = 0 while len(frames) < num_frames: vidObj.set(cv2.CAP_PROP_POS_FRAMES, count) success, image = vidObj.read() if not success: break frames.append(image) count += frame_step vidObj.release() return frames # Function to generate text descriptions for frames def generate_descriptions_for_frames(video_path, user_prompt): frames = frame_capture(video_path) images = [PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) for frame in frames] prompt = f"Describe what is happening in each of these frames in this video sequentially. {user_prompt}" images_with_prompt = [prompt] + images responses = model.generate_content(images_with_prompt) descriptions = [response.text for response in responses] formatted_description = format_descriptions(descriptions) return formatted_description # Helper function to format descriptions def format_descriptions(descriptions): return ' '.join(descriptions).strip() # Define Gradio interface video_input = gr.Video(label="Upload Video", autoplay=True) user_input = gr.Textbox(label="Ask something specific about the video", placeholder="E.g., Are there any cars in this video?") output_text = gr.Textbox(label="What's in this video") # Create Gradio app gr.Interface( fn=generate_descriptions_for_frames, inputs=[video_input, user_input], outputs=output_text, title="Interactive Video Analysis System" ).launch()