add app

assistant-streamlit.py

@@ -0,0 +1,51 @@
+import streamlit as st
+import sounddevice as sd
+import soundfile as sf
+from faster_whisper import WhisperModel
+import io
+import os
+from langchain_community.llms import Ollama
+import pyttsx3
+# Set environment variable to handle duplicate libraries
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+# Initialize WhisperModel and Ollama
+model_size = "base.en"
+model = WhisperModel(model_size, device="cpu", compute_type="int8", num_workers=5)
+llm = Ollama(model="tinyllama")
+
+# Initialize text-to-speech engine
+engine = pyttsx3.init('sapi5')
+voices = engine.getProperty('voices')
+engine.setProperty('voice',voices[0].id)
+engine.setProperty('rate',180)
+
+def speak(audio):
+    engine.say(audio)
+    engine.runAndWait()
+
+# Record and transcribe audio
+audio_data = st.audio("recorded_audio.wav", format="audio/wav", start_time=0)
+if st.button("Record"):
+    with st.spinner("Recording..."):
+        recorded_audio = sd.rec(int(5 * 44100), samplerate=44100, channels=2, dtype="int16")
+        sd.wait()
+        sf.write("recorded_audio.wav", recorded_audio, samplerate=44100)
+
+    st.audio("recorded_audio.wav", format="audio/wav", start_time=0)
+
+    # Transcribe audio and speak response
+    with open("recorded_audio.wav", "rb") as audio_file:
+        segments,info= model.transcribe(io.BytesIO(audio_file.read()), beam_size=10)
+    for segment in segments:
+        prompt=segment.text
+        print(prompt)
+        st.text(prompt)
+    if prompt:
+        response = llm.invoke(prompt)
+        st.success("Response: " + response)
+        speak(response)
+        st.stop()
+    else:
+        st.error("Failed to transcribe audio.")
+