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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 or answer a specific question
def analyze_video(video_path, user_question):
frames = frame_capture(video_path)
images = [PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) for frame in frames]
if user_question.strip():
prompt = f"Based on these video frames, {user_question}"
else:
prompt = "Describe what is happening in each of these frames in this video sequentially."
images_with_prompt = [prompt] + images
responses = model.generate_content(images_with_prompt)
descriptions = [response.text for response in responses]
return descriptions[-1] if user_question.strip() else format_descriptions(descriptions)
# Helper function to format descriptions
def format_descriptions(descriptions):
return ' '.join(descriptions).strip()
# Function to handle chat interaction
def chat_interaction(video_path, chatbot, user_message):
response = analyze_video(video_path, user_message)
chatbot.append((user_message, response))
return "", chatbot
# Define Gradio interface
video_input = gr.Video(label="Upload Video", autoplay=True)
chatbot = gr.Chatbot(label="Video Analysis Chatbot")
user_input = gr.Textbox(label="Ask something specific about the video", placeholder="E.g., Are there any cars in this video?")
# Create Gradio app
with gr.Blocks() as demo:
with gr.Column():
video_input.render()
chatbot.render()
user_input.render()
user_input.submit(fn=chat_interaction, inputs=[video_input, chatbot, user_input], outputs=[user_input, chatbot])
demo.launch()
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