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import gradio as gr
from transformers import pipeline
import numpy as np
transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
def transcribe(stream, new_chunk):
sr, y = new_chunk
y = y.astype(np.float32)
y /= np.max(np.abs(y))
if stream is not None:
stream = np.concatenate([stream, y])
else:
stream = y
return stream, transcriber({"sampling_rate": sr, "raw": stream})["text"]
demo = gr.Interface(
transcribe,
["state", gr.Audio(sources=["microphone"], streaming=True)],
["state", "text"],
live=True,
)
demo.launch()
import base64
import pandas as pd
# Example DataFrame
data = {'Column1': [1, 2], 'Column2': [3, 4]}
df = pd.DataFrame(data)
# Function to convert DataFrame to CSV and then encode to base64
def to_base64_csv(df):
csv = df.to_csv(index=False)
b64 = base64.b64encode(csv.encode()).decode()
return f"data:text/csv;base64,{b64}"
# Function to convert DataFrame to TXT and then encode to base64
def to_base64_txt(df):
txt = df.to_csv(index=False, sep='\t')
b64 = base64.b64encode(txt.encode()).decode()
return f"data:text/plain;base64,{b64}"
# Generate base64 encoded links
csv_link = to_base64_csv(df)
txt_link = to_base64_txt(df)
# Markdown format for hyperlinks in bold font with emojis
markdown_csv_link = f"**[📥 Download Dataset as CSV]({csv_link})**"
markdown_txt_link = f"**[📥 Download Dataset as TXT]({txt_link})**"
# Display as markdown (hypothetical, depends on how you render markdown in your application)
print(markdown_csv_link)
print(markdown_txt_link)
import gradio as gr
def process_live_input(input_stream):
# Process the input stream here
# Return the processed output for live update
processed_output = some_processing_function(input_stream)
return processed_output
iface = gr.Interface(fn=process_live_input,
inputs=gr.inputs.Video(source="webcam", streaming=True),
outputs="video")
iface.launch()
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