Update src/streamlit_app.py

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!
-
-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:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-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])),
-    ))
+import whisper
+import tempfile
+import os
+
+MODEL_OPTIONS = ["tiny", "base", "small", "medium", "large"]
+LANGUAGE_OPTIONS = ["en", "ta", "hi", "ml", "te"]
+
+@st.cache_resource
+def load_model(model_name):
+    return whisper.load_model(model_name)
+
+st.title("🎧 Whisper Audio Transcriber")
+st.markdown("Transcribe Tamil, English or other audio using OpenAI's Whisper model.")
+
+language = st.selectbox("🗣️ Select Language", LANGUAGE_OPTIONS, index=1)
+model_name = st.selectbox("🧠 Select Whisper Model", MODEL_OPTIONS, index=2)
+uploaded_file = st.file_uploader("🎵 Upload your audio file", type=["mp3", "wav", "m4a"])
+
+if uploaded_file:
+    model = load_model(model_name)
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
+        tmp.write(uploaded_file.read())
+        tmp_path = tmp.name
+
+    st.info(f"Transcribing with `{model_name}` model and language `{language}`...")
+
+    try:
+        result = model.transcribe(tmp_path, language=language)
+        st.success("✅ Done!")
+        st.markdown("#### 📝 Transcription Output:")
+        st.write(result["text"])
+    except Exception as e:
+        st.error(f"❌ Error: {e}")
+    finally:
+        os.remove(tmp_path)