requirements, app.py

Dockerfile

@@ -2,6 +2,9 @@ FROM python:3.9-slim
 
 WORKDIR /app
 
+RUN mkdir -p /.streamlit && chmod 777 /.streamlit
+RUN mkdir -p /tmp && chmod 777 /tmp
+
 RUN apt-get update && apt-get install -y \
     build-essential \
     curl \
@@ -14,8 +17,6 @@ COPY src/ ./src/
 
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+EXPOSE 7860
+HEALTHCHECK CMD curl --fail http://localhost:7860/_stcore/health
+ENTRYPOINT ["streamlit", "run", ..., "--server.port=7860"]

requirements.txt

@@ -1,3 +1,6 @@
 altair
 pandas
-streamlit
+streamlit
+openai-whisper
+torch
+torchaudio

src/streamlit_app.py

@@ -1,40 +1,37 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
+import os
+os.environ["STREAMLIT_HOME"] = "/tmp"
+os.environ["MPLCONFIGDIR"] = "/tmp"
+os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
+os.environ["STREAMLIT_WATCHER_TYPE"] = "none"
+
+import asyncio
+try:
+    asyncio.get_running_loop()
+except RuntimeError:
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+import streamlit as st
+import whisper
+import uuid
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+st.title("🧪 Whisper Tiny ASR (No Diarization)")
+st.write("Upload an audio file (wav/mp3/m4a) and get a quick transcription using Whisper Tiny.")
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+uploaded = st.file_uploader("Choose an audio file", type=["wav", "mp3", "m4a", "flac"])
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+if uploaded:
+    audio_id = uuid.uuid4().hex
+    audio_path = f"/tmp/audio_{audio_id}.wav"
+    with open(audio_path, "wb") as f:
+        f.write(uploaded.read())
+    st.success(f"✅ File saved to {audio_path}")
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+    if st.button("Transcribe"):
+        with st.spinner("Loading Whisper (tiny)..."):
+            model = whisper.load_model("tiny").to("cpu")
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+        with st.spinner("Transcribing..."):
+            result = model.transcribe(audio_path, language="id")
+            st.header("📝 Transcription Result")
+            st.markdown(result["text"])