app.py

# import gradio as gr
# import os

# HF_TOKEN = os.getenv('HW_Token')
# hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "save_audio")

# import gradio as gr
# import os

# class TextFileReader:
#     def __init__(self):
#         self.lines = []
#         self.current_index = 0

#     def read_lines(self, file):
#         self.lines = file.decode('utf-8').splitlines()
#         self.current_index = 0
#         return self.get_current_line()

#     def get_current_line(self):
#         if 0 <= self.current_index < len(self.lines):
#             return self.lines[self.current_index]
#         else:
#             return "End of file reached."

#     def forward_line(self):
#         self.current_index = min(self.current_index + 1, len(self.lines) - 1)
#         return self.get_current_line()

#     def backward_line(self):
#         self.current_index = max(self.current_index - 1, 0)
#         return self.get_current_line()

# reader = TextFileReader()

# # Define a function to save the text lines to a file
# def save_text_lines(file):
#     lines = reader.read_lines(file)
#     with open("text_lines.txt", "w") as f:
#         f.write("\n".join(reader.lines))
#     return lines

# # Define a function to save the audio file and corresponding text
# def save_audio_text(audio, text):
#     if not os.path.exists("recordings"):
#         os.makedirs("/recordings")

#     # Debugging to print out the structure of the audio variable
#     print("Received audio data:", audio)

#     # Check if audio is a dictionary and contains 'data'
#     if isinstance(audio, dict) and 'data' in audio:
#         audio_data = audio['data']
#         audio_path = f"/recordings/line_{reader.current_index}.wav"
#         text_path = f"/recordings/line_{reader.current_index}.txt"
        
#         with open(audio_path, "wb") as f:
#             f.write(audio_data)
            
#         with open(text_path, "w") as f:
#             f.write(text)
        
#         # Move to the next line after saving
#         next_line = reader.forward_line()
#         return next_line
#     else:
#         return "Audio data is not in the expected format."

# # Define the Gradio interface
# with gr.Blocks() as demo:
#     with gr.Row():
#         file_upload = gr.File(label="Upload a text file", type="binary")
#         generate_button = gr.Button("Generate Lines")
    
#     current_line = gr.Textbox(label="Current Line")
    
#     def update_output(file):
#         lines = reader.read_lines(file)
#         save_text_lines(file)  # Save the text lines to a file
#         return lines

#     generate_button.click(fn=update_output, inputs=file_upload, outputs=current_line)
    
#     with gr.Row():
#         audio_record = gr.Audio(sources=["microphone","upload"], type="filepath")
#         save_button = gr.Button("Save Audio and Next Line")

#     save_button.click(fn=save_audio_text, inputs=[audio_record, current_line], outputs=current_line)

# demo.launch()

# import gradio as gr


# def calculator(num1, operation, num2):
#     if operation == "add":
#         return num1 + num2
#     elif operation == "subtract":
#         return num1 - num2
#     elif operation == "multiply":
#         return num1 * num2
#     elif operation == "divide":
#         return num1 / num2


# iface = gr.Interface(
#     calculator,
#     ["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
#     "number",
#     allow_flagging="manual",
#     flagging_options=["correct", "wrong"]
# )

# iface.launch()

# import os

# HF_TOKEN = os.getenv('HF_TOKEN')
# hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced")

# iface = gr.Interface(
#     calculator,
#     ["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
#     "number",
#     description="Check out the crowd-sourced dataset at: [https://huggingface.co/Sajjo/crowdsourced](https://huggingface.co/Sajjo/crowdsourced)",
#     allow_flagging="manual",
#     flagging_options=["wrong sign", "off by one", "other"],
#     flagging_callback=hf_writer
# )

# iface.launch()

# import numpy as np
# import gradio as gr

# def sepia(input_img, strength):
#     sepia_filter = strength * np.array(
#         [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]
#     ) + (1-strength) * np.identity(3)
#     sepia_img = input_img.dot(sepia_filter.T)
#     sepia_img /= sepia_img.max()
#     return sepia_img

# callback = gr.CSVLogger()

# with gr.Blocks() as demo:
#     with gr.Row():
#         with gr.Column():
#             img_input = gr.Image()
#             strength = gr.Slider(0, 1, 0.5)
#         img_output = gr.Image()
#     with gr.Row():
#         btn = gr.Button("Flag")
        
#     # This needs to be called at some point prior to the first call to callback.flag()
#     callback.setup([img_input, strength, img_output], "flagged_data_points")

#     img_input.change(sepia, [img_input, strength], img_output)
#     strength.change(sepia, [img_input, strength], img_output)
    
#     # We can choose which components to flag -- in this case, we'll flag all of them
#     btn.click(lambda *args: callback.flag(args), [img_input, strength, img_output], None, preprocess=False)

# demo.launch()

# import gradio as gr
# import os
# import wave
# import tempfile
# import numpy as np

# # Global variables to store file and line index
# file_index = 0
# line_index = 0
# lines = []

# # Hugging Face token and dataset saver
# HF_TOKEN = os.getenv('HF_TOKEN')
# hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced-calculator-demo")

# # Function to read lines from a file
# def read_lines_from_file(file_path):
#     global lines
#     with open(file_path, 'r') as file:
#         lines = file.readlines()

# # Function to save audio to a WAV file
# def save_audio_to_file(audio):
#     sample_rate, data = audio  # audio is a tuple (sample_rate, data)
    
#     # Save the audio data as a WAV file in a temporary location
#     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
#         with wave.open(tmp_file.name, 'wb') as wav_file:
#             wav_file.setnchannels(1)  # Mono audio
#             wav_file.setsampwidth(2)  # 2 bytes per sample (16-bit PCM)
#             wav_file.setframerate(sample_rate)
#             wav_file.writeframes(data.tobytes())
        
#         # Return the path to the saved WAV file
#         return tmp_file.name

# # Function to save data to the Hugging Face dataset
# def save_to_hf_dataset(text, audio_path):
#     with open(audio_path, "rb") as f:
#         audio_data = f.read()
#     hf_writer.save({"text": text, "audio": audio_data})

# # Gradio interface function
# def audio_capture_interface():
#     global file_index, line_index, lines
    
#     # Initial file to read
#     files = os.listdir('./audio_samples')
#     read_lines_from_file(os.path.join('./audio_samples', files[file_index]))

#     # Define the interface components
#     audio_input = gr.Audio(source="microphone", type="numpy", label="Speak and click submit")
#     output_text = gr.Textbox(label="Status", placeholder="Status will appear here")

#     # Function to capture and process the audio input
#     def process_audio(audio):
#         global line_index, lines
        
#         try:
#             text_line = lines[line_index].strip()
#             file_path = save_audio_to_file(audio)
#             save_to_hf_dataset(text_line, file_path)
#             return f"Audio saved to {file_path} and uploaded to Hugging Face Dataset."
#         except Exception as e:
#             return f"Error saving audio: {str(e)}"

#     # Function to handle navigation buttons
#     def navigate_lines(button):
#         global line_index, lines
        
#         if button == 'forward':
#             line_index = min(line_index + 1, len(lines) - 1)
#         elif button == 'previous':
#             line_index = max(line_index - 1, 0)
        
#         output_text.value = lines[line_index]

#     # Create the Gradio interface
#     with gr.Blocks() as iface:
#         with gr.Row():
#             gr.Textbox(label="Text", value=lines[line_index], interactive=False)
#         with gr.Row():
#             audio_input.render()
#         with gr.Row():
#             gr.Button("Previous").click(lambda: navigate_lines('previous'), outputs=output_text)
#             gr.Button("Forward").click(lambda: navigate_lines('forward'), outputs=output_text)
#             gr.Button("Submit").click(process_audio, inputs=audio_input, outputs=output_text)

#     return iface

# # Launch the interface
# iface = audio_capture_interface()
# iface.launch()

import gradio as gr
import os
import wave
import tempfile
import numpy as np

# Global variables to store line index and lines
line_index = 0
lines = []

# Hugging Face token and dataset saver
HF_TOKEN = os.getenv('HF_TOKEN')
hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced-calculator-demo")

# Function to read lines from a file
def read_lines_from_file(file_path):
    global lines, line_index
    with open(file_path, 'r') as file:
        lines = file.readlines()
    line_index = 0  # Reset line index when a new file is loaded
    return lines[line_index].strip() if lines else "No lines found in the file."

# Function to save audio to a WAV file
def save_audio_to_file(audio):
    sample_rate, data = audio  # audio is a tuple (sample_rate, data)
    
    # Save the audio data as a WAV file in a temporary location
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
        with wave.open(tmp_file.name, 'wb') as wav_file:
            wav_file.setnchannels(1)  # Mono audio
            wav_file.setsampwidth(2)  # 2 bytes per sample (16-bit PCM)
            wav_file.setframerate(sample_rate)
            wav_file.writeframes(data.tobytes())
        
        # Return the path to the saved WAV file
        return tmp_file.name

# Function to save data to the Hugging Face dataset
def save_to_hf_dataset(text, audio_path):
    with open(audio_path, "rb") as f:
        audio_data = f.read()
    hf_writer.save({"text": text, "audio": audio_data})

# Function to capture and process the audio input
def process_audio(audio):
    global line_index, lines
    try:
        text_line = lines[line_index].strip()
        file_path = save_audio_to_file(audio)
        save_to_hf_dataset(text_line, file_path)
        return f"Audio saved to {file_path} and uploaded to Hugging Face Dataset."
    except Exception as e:
        return f"Error saving audio: {str(e)}"

# Function to handle navigation buttons
def navigate_lines(button):
    global line_index, lines
    if button == 'forward':
        line_index = min(line_index + 1, len(lines) - 1)
    elif button == 'previous':
        line_index = max(line_index - 1, 0)
    return lines[line_index].strip() if lines else "No lines found."

# Function to handle file upload
def upload_file(file):
    if file is not None:
        file_path = file.name
        return read_lines_from_file(file_path)
    else:
        return "No file uploaded."

# Gradio interface function
def audio_capture_interface():
    with gr.Blocks() as iface:
        with gr.Row():
            file_upload = gr.File(label="Upload a text file", file_types=["text"])
            text_display = gr.Textbox(label="Text", value="Please upload a file to begin.", interactive=False)
        with gr.Row():
            audio_input = gr.Audio(sources=["microphone","upload"], type="filepath", label="Speak and click submit")
        with gr.Row():
            status_output = gr.Textbox(label="Status", placeholder="Status will appear here")
        with gr.Row():
            gr.Button("Previous").click(lambda: navigate_lines('previous'), None, text_display)
            gr.Button("Forward").click(lambda: navigate_lines('forward'), None, text_display)
            gr.Button("Submit").click(process_audio, inputs=audio_input, outputs=status_output)

        file_upload.upload(upload_file, outputs=text_display)

    return iface

# Launch the interface
iface = audio_capture_interface()
iface.launch()