app.py

import gradio as gr
from gradio_client import Client
import cv2
from moviepy.editor import *

# 1. extract and store 1 image every 5 images from video input
# 2. extract audio
# 3. for each image from extracted_images, get caption from caption model and concatenate into list
# 4. for audio, ask audio questioning model to describe sound/scene
# 5. give all to LLM, and ask it to resume, according to image caption list combined to audio caption

import re
import torch
from transformers import pipeline

zephyr_model = "HuggingFaceH4/zephyr-7b-beta"
pipe = pipeline("text-generation", model=zephyr_model, torch_dtype=torch.bfloat16, device_map="auto")

standard_sys = f"""

"""

def extract_frames(video_in, interval=12, output_format='.jpg'):
    """Extract frames from a video at a specified interval and store them in a list.

    Args:
    - video_in: string or path-like object pointing to the video file
    - interval: integer specifying how many frames apart to extract images (default: 5)
    - output_format: string indicating desired format for saved images (default: '.jpg')

    Returns:
    A list of strings containing paths to saved images.
    """

    # Initialize variables
    vidcap = cv2.VideoCapture(video_in)
    frames = []
    count = 0

    # Loop through frames until there are no more
    while True:
        success, image = vidcap.read()

        # Check if successful read and not past end of video
        if success:
            print('Read a new frame:', success)

            # Save current frame if it meets criteria
            if count % interval == 0:
                filename = f'frame_{count // interval}{output_format}'
                frames.append(filename)
                cv2.imwrite(filename, image)
                print(f'Saved {filename}')

            # Increment counter
            count += 1

        # Break out of loop when done reading frames
        else:
            break

    # Close video capture
    vidcap.release()
    print('Done extracting frames!')

    return frames

def process_image(image_in):
    client = Client("vikhyatk/moondream2")
    result = client.predict(
		image_in,	# filepath  in 'image' Image component
		"Describe precisely the image in one sentence.",	# str  in 'Question' Textbox component
		api_name="/answer_question"
        #api_name="/predict"
    )
    print(result)
    return result

def extract_audio(video_path):
    # Open the video clip and extract the audio stream
    audio = VideoFileClip(video_path).audio
    
    # Set the output file path and format
    output_path = 'output_audio.wav'
    
    # Write the audio stream to disk using the AAC codec
    audio.write_audiofile(output_path, codec='aac')
    
    # Confirm that the audio file was written successfully
    if os.path.exists(output_path):
        print(f'Successfully wrote audio to {output_path}.')
        return output_path
    else:
        raise FileNotFoundError(f'Failed to write audio to {output_path}.')

def get_salmonn(audio_in):
    salmonn_prompt = "Please list each event in the audio in order."
    client = Client("fffiloni/SALMONN-7B-gradio")
    result = client.predict(
    		audio_in,	# filepath  in 'Audio' Audio component
    		salmonn_prompt,	# str  in 'User question' Textbox component
    		4,	# float (numeric value between 1 and 10) in 'beam search numbers' Slider component
    		1,	# float (numeric value between 0.8 and 2.0) in 'temperature' Slider component
    		0.9,	# float (numeric value between 0.1 and 1.0) in 'top p' Slider component
    		api_name="/gradio_answer"
    )
    print(result)
    return result

def llm_process(user_prompt):
    agent_maker_sys = standard_sys
    
    instruction = f"""
<|system|>
{agent_maker_sys}</s>
<|user|>
"""
    
    prompt = f"{instruction.strip()}\n{user_prompt}</s>"    
    outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
    pattern = r'\<\|system\|\>(.*?)\<\|assistant\|\>'
    cleaned_text = re.sub(pattern, '', outputs[0]["generated_text"], flags=re.DOTALL)
    
    print(f"SUGGESTED video description: {cleaned_text}")
    return cleaned_text.lstrip("\n")

def infer(video_in):
    # Extract frames from a video
    frame_files = extract_frames(video_in)
    
    # Process each extracted frame and collect results in a list
    processed_texts = []
    for frame_file in frame_files:
        text = process_image(frame_file)
        processed_texts.append(text)
    print(processed_texts)

    # Convert processed_texts list to a string list with line breaks
    string_list = '\n'.join(processed_texts)

    # Extract audio from video
    extracted_audio = extract_audio(video_in)

    # Get description of audio content
    audio_content_described = get_salmonn(extracted_audio)

    # Assemble captions
    formatted_captions = f"""
### Visual events:\n{string_list}\n ### Audio events:\n{audio_content_described}
"""
    print(formatted_captions)

    # Send formatted captions to LLM
    #video_description_from_llm = llm_process(formatted_captions)
    return formatted_captions

with gr.Blocks() as demo :
    with gr.Column(elem_id="col-container"):
        gr.HTML("""
        <h2 style="text-align: center;">Video description</h2>
        """)
        video_in = gr.Video(label="Video input")
        submit_btn = gr.Button("Submit")
        video_description = gr.Textbox(label="Video description")
    submit_btn.click(
        fn = infer,
        inputs = [video_in],
        outputs = [video_description]
    )
demo.queue().launch()