Update app.py

app.py

@@ -2,60 +2,64 @@ import streamlit as st
 from transformers import pipeline
 
 
-model = pipeline("sentiment-analysis")
-st.title("Hugging Face Model Demo")
-input_text = st.text_input("Enter your text", "")
-if st.button("Analyze"):
-    # Perform inference using the loaded model
-    result = model(input_text)
-    st.write("Prediction:", result[0]['label'], "| Score:", result[0]['score'])
-
-###
-# def recording_storing():
-#     chunk = 1024  # Record in chunks of 1024 samples
-#     sample_format = pyaudio.paInt16  # 16 bits per sample
-#     channels = 1
-#     fs = 44100  # Record at 44100 samples per second
-#     seconds = 4
-#     # filename = "output.wav"
-
-#     p = pyaudio.PyAudio()  # Create an interface to PortAudio
-
-#     print('Recording')
-
-#     stream = p.open(format=sample_format,
-#                     channels=channels,
-#                     rate=fs,
-#                     frames_per_buffer=chunk,
-#                     input=True)
-
-#     frames = []  # Initialize array to store frames
-
-#     # Store data in chunks for 3 seconds
-#     for i in range(0, int(fs / chunk * seconds)):
-#         data = stream.read(chunk)
-#         frames.append(data)
-
-#     # Stop and close the stream 
-#     stream.stop_stream()
-#     stream.close()
-#     # Terminate the PortAudio interface
-#     p.terminate()
-
-#     print('Finished recording')
-
-#     # Save the recorded data as a WAV file
-#     wf = wave.open(filename, 'wb')
-#     wf.setnchannels(channels)
-#     wf.setsampwidth(p.get_sample_size(sample_format))
-#     wf.setframerate(fs)
-#     wf.writeframes(b''.join(frames))
-#     wf.close()
-
-    
-    
-#     return "recorded"
-
-# audio = recording_storing()
-# print(audio)
+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 = 0.007
+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, inputs=input_audio, outputs=output_text)
+
+
+demo.launch()
+
+
+
+########################     models
+# model = pipeline("sentiment-analysis")
+# st.title("Hugging Face Model Demo")
+# input_text = st.text_input("Enter your text", "")
+# if st.button("Analyze"):
+#     # Perform inference using the loaded model
+#     result = model(input_text)
+#     st.write("Prediction:", result[0]['label'], "| Score:", result[0]['score'])
+
+
+