Update app.py

app.py

@@ -1,49 +1,31 @@
 import streamlit as st
-from pynput import keyboard
-import transformers
-from transformers import WhisperFeatureExtractor, WhisperForConditionalGeneration
-import soundfile as sf
+from keyboard import on_press, wait
 
 # Define voice, speed, and pitch variables (initial values)
 voice = "en"  # English (change for other voices)
 speed = 1.0
 pitch = 1.0
 
-# Initialize feature extractor and model from Hugging Face Transformers
-feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper")
-model = WhisperForConditionalGeneration.from_pretrained("openai/whisper")
-
-
-def on_press(key):
-    global voice, speed, pitch
-
-    # Handle special keys (consider modifications for your needs)
-    if key == keyboard.Key.esc:
-        return False  # Exit keystroke listener
-
+# (Optional) Text pre-processing function (customize for specific needs)
+def preprocess_text(key):
     pressed_key = str(key).replace("'", "")
+    return pressed_key
 
-    # Text pre-processing (optional, customize for specific needs)
-    text = f"{pressed_key}"
-
-    # Encode text
-    inputs = feature_extractor(text, return_tensors="pt")
-
-    # Generate speech tokens with adjusted voice, speed, and pitch
-    generation = model.generate(
-        **inputs, voice=voice, speed=speed, pitch=pitch
-    )
+# Function to handle key presses and trigger text-to-speech
+def on_press_wrapper(key):
+    global voice, speed, pitch
 
-    # Convert generated tokens to audio waveform
-    audio_output = model.to_audio(generation)
+    # Call pre-processing function (if implemented)
+    text = preprocess_text(key)
 
-    # Play the audio (replace with your preferred audio playback library)
-    sf.write("output.wav", audio_output, samplerate=16000)
-    st.audio("output.wav", format="audio/wav")
+    # Simulate text input to a text-to-speech API (replace with your chosen API)
+    # This example demonstrates a basic placeholder
+    print(f"Simulating text input to TTS API: {text} (Voice: {voice}, Speed: {speed}, Pitch: {pitch})")
 
+    # You'll need to integrate your chosen text-to-speech API here
 
 # Streamlit App
-st.title("Text-to-Speech Keystroke Announcer")
+st.title("Text-to-Speech Keystroke Announcer (Headless)")
 
 # User Interface for customization options
 voice_selected = st.selectbox("Voice", ["en", "fr", "es"])  # Add more options
@@ -57,7 +39,7 @@ pitch = pitch_slider
 
 # Start keystroke listener on button press
 if st.button("Start Keystroke Announcer"):
-    with keyboard.Listener(on_press=on_press) as listener:
-        listener.join()
+    on_press(on_press_wrapper)
+    wait()  # Block execution until the listener is stopped
 
-st.write("Press 'Esc' to stop keystroke detection.")
+st.write("Press 'Esc' to stop keystroke detection (simulated).")