import gradio as gr import pandas as pd import os import gradio as gr css = """#myProgress { width: 100%; background-color: gray; border-radius: 2px; } #myBar { width: 0%; height: 30px; background-color: blue; border-radius: 2px; } #progressText { position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); color: white; font-weight: bold; font-size: 14px; """ js_progress_bar = """ function move(n_ann, total_ann) { var elem = document.getElementById("myBar"); elem.style.width = n_ann/total_ann * 100 + "%"; progressText.innerText = 'Completed: ' + n_ann + '/' + total_ann } """ # List of all audio files to annotate file_list = pd.read_excel(os.path.join('combined_annotations.xlsx')) total_annotations = len(file_list) # Initialize an empty DataFrame to store annotations annotations = pd.DataFrame(columns=['sample_id', 'sentence', 'emotion', 'confidence', 'comments']) current_index = {"index": 0} # Dictionary to allow modifying inside functions def load_example(index): """Loads the example in row #index from dataframe file_list. If there are any annotations it will give those values to the annotation dataframe""" row = file_list.iloc[index] audio_path = os.path.join('files_to_annotate_padded_smaller_emotion_set', row["SAMPLE ID"].split('-')[0], row["SAMPLE ID"] + '.wav') print(f"Audio path: {audio_path}, Exists: {os.path.exists(audio_path)}") sentence = row["SENTENCE"] # If the user already made an annotation for this example, gradio will return said annotation previous_annotation = ( annotations.iloc[index].to_dict() if index < len(annotations) else {"sample_id": row["SAMPLE ID"], "emotion": '', "confidence": 0, "comments": ''} ) return (sentence, audio_path, previous_annotation['emotion'], previous_annotation['confidence'], current_index['index'] + 1, previous_annotation["comments"]) def save_annotation(emotions, confidence, comments): """Save the annotation for the current example.""" idx = current_index["index"] row = file_list.iloc[idx] sample_id = row["SAMPLE ID"] sentence = row["SENTENCE"] # Update or append annotation if sample_id in annotations["sample_id"].values: annotations.loc[annotations["sample_id"] == sample_id, ["emotion", "confidence", "comments"]] = \ [emotions, confidence, comments] else: annotations.loc[len(annotations)] = [sample_id, sentence, emotions, confidence, comments] ann_completed.value += 1 annotations.to_csv("annotations.csv", index=False) # Save to a CSV file def next_example(emotions, confidence, comments): """Move to the next example.""" if emotions == "Blank": raise gr.Warning("Please fill out the emotion section") else: save_annotation(emotions, confidence, comments) if current_index["index"] < len(file_list) - 1: current_index["index"] += 1 return load_example(current_index["index"]) def previous_example(emotion, confidence, comments): """Move to the previous example.""" if emotion.value != "Blank": save_annotation(emotion, confidence, comments) if current_index["index"] > 0: current_index["index"] -= 1 return load_example(current_index["index"]) return load_example(current_index["index"]) # =================== # Gradio Interface # =================== audio_path = 'test.mp3' with (gr.Blocks(theme=gr.themes.Soft(), css = css) as demo): participant_id = gr.Textbox(label='What is your participant ID?', interactive = True) lets_go = gr.Button("Let's go!") ann_completed = gr.Number(1, visible=False) total = gr.Number(total_annotations, visible=False) # Row with progress bar gr.HTML("""