Delete both_app.py

both_app.py

@@ -1,186 +0,0 @@
-import warnings
-warnings.filterwarnings("ignore")
-import gradio as gr
-import pandas as pd
-import torch
-import gc
-import os
-from typing import Dict, List
-from src.video_model import describe_video  
-from src.utils import parse_string, parse_annotations
-
-# --- Function to process single video ---
-def process_video_and_questions(video, standing, hands, location, screen):
-    video_name = os.path.basename(video)
-    query = f"Answer the questions from the video\n"
-    additional_info = []
-    if standing:
-        additional_info.append("Is the subject in the video standing or sitting?\n")
-    if hands:
-        additional_info.append("Is the subject holding any object in their hands?\n")
-    if location:
-        additional_info.append("Is the subject present indoors?\n")
-    if screen:
-        additional_info.append("Is the subject interacting with a screen in the background by facing the screen?\n")
-    
-    end_query = """Provide the results in <annotation> tags, where 0 indicates False, 1 indicates True, and None indicates that no information is present. Follow the below examples\n:
-        <annotation>indoors: 0</annotation>
-        <annotation>standing: 1</annotation>
-        <annotation>hands.free: 0</annotation>
-        <annotation>screen.interaction_yes: 0</annotation>
-        """
-
-    final_query = query + " " + " ".join(additional_info)
-    final_prompt = final_query + " " + end_query
-    
-    response = describe_video(video, final_prompt)
-    final_response = f"<video_name>{video_name}</video_name>" + " \n" + response
-
-    conditions = {
-        'standing': (standing, 'standing: 1', 'standing: None'),
-        'hands': (hands, 'hands.free: 1', 'hands.free: None'),
-        'location': (location, 'indoors: 1', 'indoors: None'),
-        'screen': (screen, 'screen.interaction_yes: 1', 'screen.interaction_yes: None')
-    }
-    
-    for key, (condition, to_replace, replacement) in conditions.items():
-        if not condition:
-            final_response = final_response.replace(to_replace, replacement)
-    
-    return final_response 
-
-# Function to save data to a CSV file using pandas
-def save_to_csv(observations: List[Dict], output_dir: str = "outputs") -> str:
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir)
-    
-    # Convert the list of dictionaries to a pandas DataFrame
-    df = pd.DataFrame(observations)
-    
-    # Specify the CSV file path
-    csv_file = os.path.join(output_dir, "video_observations.csv")
-    
-    # Save the DataFrame to a CSV file
-    df.to_csv(csv_file, index=False)
-    
-    return csv_file
-
-# Function to process a single video and return the observation data
-def process_single_video(video_path, standing, hands, location, screen) -> Dict:
-    video_name = os.path.basename(video_path)  # Extract video name from the path
-    query = "Describe this video in detail and answer the questions"
-    additional_info = []
-    if standing:
-        additional_info.append("Is the subject in the video standing or sitting?\n")
-    if hands:
-        additional_info.append("Is the subject holding any object in their hands?\n")
-    if location:
-        additional_info.append("Is the subject present indoors?\n")
-    if screen:
-        additional_info.append("Is the subject interacting with a screen in the background by facing the screen?\n")
-    
-    end_query = """Provide the results in <annotation> tags, where 0 indicates False, 1 indicates True, and None indicates that no information is present. Follow the below examples:
-        <annotation>indoors: 0</annotation>
-        <annotation>standing: 1</annotation>
-        <annotation>hands.free: 0</annotation>
-        <annotation>screen.interaction_yes: 0</annotation>
-        """
-    
-    final_query = query + " " + " ".join(additional_info)
-    final_prompt = final_query + " " + end_query
-    
-    # Assuming your describe_video function handles the video processing
-    response = describe_video(video_path, final_prompt)
-    final_response = f"<video_name>{video_name}</video_name>" + " \n" + response
-
-    conditions = {
-        'standing': (standing, 'standing: 1', 'standing: None'),
-        'hands': (hands, 'hands.free: 1', 'hands.free: None'),
-        'location': (location, 'indoors: 1', 'indoors: None'),
-        'screen': (screen, 'screen.interaction_yes: 1', 'screen.interaction_yes: None')
-    }
-    
-    for key, (condition, to_replace, replacement) in conditions.items():
-        if not condition:
-            final_response = final_response.replace(to_replace, replacement)
-
-    # Parse the response to extract video name and annotations
-    parsed_content = parse_string(final_response, ["video_name", "annotation"])
-    video_name = parsed_content['video_name'][0] if parsed_content['video_name'] else None
-    annotations_dict = parse_annotations(parsed_content['annotation']) if parsed_content['annotation'] else {}
-
-    # Return the observation as a dictionary
-    return {'video_name': video_name, **annotations_dict}
-
-# Function to process all videos in a folder
-def process_multiple_videos(video_files: List[str], standing, hands, location, screen):
-    all_observations = []
-
-    for video_path in video_files:
-        observation = process_single_video(video_path, standing, hands, location, screen)
-        if observation['video_name']:  # Only add valid observations
-            all_observations.append(observation)
-        else:
-            print("Error processing video:", video_path)  # Log any errors
-
-        # Clear GPU cache
-        torch.cuda.empty_cache()
-        gc.collect() 
-
-    # Save all observations to a CSV file and return the file path
-    csv_file = save_to_csv(all_observations)
-    return "Processing completed. Download the CSV file.", csv_file
-
-# Gradio interface
-def gradio_interface_single(video, standing, hands, location, screen):
-    return process_video_and_questions(video, standing, hands, location, screen)
-
-def gradio_interface_multiple(video_files, standing, hands, location, screen):
-    video_file_paths = [video.name for video in video_files]  # Extract file paths from uploaded files
-    return process_multiple_videos(video_file_paths, standing, hands, location, screen)
-
-with gr.Blocks() as demo:
-    with gr.Tab("Single Video Processing"):
-        with gr.Row():
-            with gr.Column():
-                video = gr.Video(label="Video")
-                standing = gr.Checkbox(label="Standing")
-                hands = gr.Checkbox(label="Hands Free")
-                location = gr.Checkbox(label="Indoors")
-                screen = gr.Checkbox(label="Screen Interaction")
-                submit_btn = gr.Button("Generate Annotations")
-                generate_csv_btn = gr.Button("Generate CSV")
-            
-            with gr.Column():
-                response = gr.Textbox(label="Video Description", show_label=True, show_copy_button=True)
-                csv_output = gr.File(label="Download CSV", interactive=False)
-        
-        submit_btn.click(
-            fn=gradio_interface_single,
-            inputs=[video, standing, hands, location, screen],
-            outputs=response
-        )
-        
-        generate_csv_btn.click(
-            fn=save_to_csv,
-            inputs=response,
-            outputs=csv_output
-        )
-    
-    with gr.Tab("Batch Video Processing"):
-        with gr.Row():
-            video_files = gr.File(file_count="multiple", file_types=["video"], label="Upload multiple videos")
-            standing = gr.Checkbox(label="Standing")
-            hands = gr.Checkbox(label="Hands Free")
-            location = gr.Checkbox(label="Indoors")
-            screen = gr.Checkbox(label="Screen Interaction")
-            submit_btn = gr.Button("Process Videos")
-            download_link = gr.File(label="Download CSV")
-        
-        submit_btn.click(
-            fn=gradio_interface_multiple,
-            inputs=[video_files, standing, hands, location, screen],
-            outputs=[response, download_link]
-        )
-
-demo.launch(debug=False)