Update app.py

app.py

@@ -1,54 +1,22 @@
 import streamlit as st
 from transformers import pipeline
 
-from scipy.io.wavfile import write
-import gradio as gr
-import wavio
-
-input_file = 'recorded.wav'
-# output_file = 'output_filtered_receiver.wav'
-
-low_frequency = 18000
-high_frequency = 19000
-bit_duration = 0.010
-sample_rate = 44100
-amplitude_scaling_factor = 10.0
-
-def record(audio):
-    """
-    This function records audio and writes it to a .wav file.
-    Parameters:
-    audio (tuple): A tuple containing the sample rate and the audio data.
-    Returns:
-    str: A success message if the audio is recorded correctly, otherwise an error message.
-    """
-    try:
-        # Check if the audio tuple contains exactly two elements
-        if len(audio) != 2:
-            return f"Error: Expected a tuple with 2 elements, but got {len(audio)}"
-
-        # Unpack the sample rate and data from the audio tuple
-        sr, data = audio
-
-        # Write the audio data to a .wav file
-        wavio.write("recorded.wav", data, sr)
-
-        # Call the filtered function to apply the bandpass filter to the audio data
-        filtered()
-
-        # Return a success message
-        return f"Audio receive correctly"
-    except Exception as e:
-        # If an error occurs, return an error message
-        return f"Error: {str(e)}"
-
-with gr.Blocks() as demo:
-    btn_record = gr.Button(value="record")
-    btn_record.click(fn=record)
-
-
-demo.launch()
 
+import os
+import io
+import tempfile
+import base64
+from audiorecorder import audiorecorder
+from openai import OpenAI
+client = OpenAI()
+st.title("Whisper App")
+audio = audiorecorder("Click to record", "Click to stop recording")
+
+if len(audio) > 0:
+    temp_dir = tempfile.mkdtemp()
+    temp_file_path = os.path.join(temp_dir, 'temp_audio.mp3')
+    audio.export(temp_file_path, format="mp3")
+    
 
 
 ########################     models