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()