Update app.py

app.py

@@ -1,47 +1,17 @@
 from warnings import filterwarnings
 filterwarnings("ignore")
-from transformers import pipeline
-import torch
-import pyaudio
-import wave
+import gradio as gr
 
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-classifier = pipeline(
-    "audio-classification", model="MIT/ast-finetuned-speech-commands-v2", device=device
-)
+def function2(x):
+    print("Triggered 2")
 
-def record_audio(chunk_length_s=2.0, stream_chunk_s=0.25):
-    audio = pyaudio.PyAudio()
-    stream = audio.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=int(16000 * stream_chunk_s))
-    frames = []
-    for _ in range(int(chunk_length_s / stream_chunk_s)):
-        data = stream.read(int(16000 * stream_chunk_s))
-        frames.append(data)
-    stream.stop_stream()
-    stream.close()
-    audio.terminate()
-    return b''.join(frames)
+def function1(x):
+    if x == "1":  # Convert x to a string for comparison
+        print("Triggered 1")
+        with gr.Blocks():
+            demo1 = gr.Interface(fn=function2, inputs=gr.Textbox(label="Input for Function 2"), outputs=[], title="Function 2 Interface")
+            demo1.launch(share=True, debug=True)
 
-def launch_fn(
-    wake_word="marvin",
-    prob_threshold=0.5,
-    chunk_length_s=2.0,
-    stream_chunk_s=0.25,
-    debug=False,
-):
-    if wake_word not in classifier.model.config.label2id.keys():
-        raise ValueError(
-            f"Wake word {wake_word} not in set of valid class labels, pick a wake word in the set {classifier.model.config.label2id.keys()}."
-        )
-    audio_data = record_audio(chunk_length_s, stream_chunk_s)
-    prediction = classifier(audio_data)
-    prediction = prediction[0]
-    if debug:
-        print(prediction)
-    if prediction["label"] == wake_word:
-        if prediction["score"] > prob_threshold:
-            return True
-    return False
-
-if __name__ == "__main__":
-    launch_fn(debug=True)
+with gr.Blocks():
+    demo = gr.Interface(fn=function1, inputs=gr.Textbox(label="Input for Function 1"), outputs=[], title="Function 1 Interface")
+    demo.launch(share=True, debug=True)