Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,32 @@
-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 numpy as np
+import joblib
+from PIL import Image, ImageOps
+
+st.title('Handwritten Digit Recognizer')
+
+# Load the model
+try:
+    model = joblib.load('digit_rf_model.joblib')
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+
+uploaded_file = st.file_uploader("Upload a digit image (28x28 grayscale)", type=["png", "jpg", "jpeg"])
+
+def preprocess_image(img):
+    # Convert to grayscale, resize to 28x28, flatten
+    img = ImageOps.grayscale(img)
+    img = img.resize((28, 28))
+    arr = np.array(img).reshape(1, -1)
+    return arr
+
+if uploaded_file is not None:
+    try:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        input_data = preprocess_image(image)
+        if st.button('Predict Digit'):
+            prediction = model.predict(input_data)
+            st.success(f'Predicted Digit: {int(prediction[0])}')
+    except Exception as e:
+        st.error(f"Error processing image or making prediction: {e}")