Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,40 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from tensorflow.keras.models import load_model
+from PIL import Image, ImageOps
+import numpy as np
 
-"""
-# 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])),
-    ))
+model = load_model('src/sign_model.h5')
+
+def process_image(img):
+    img = img.convert('L')
+    img = img.resize((28, 28))
+    img = np.array(img)
+    img = img / 255.0
+    
+    # Reshape to (1, 28, 28, 1)
+    img = img.reshape(1, 28, 28, 1)
+    return img
+
+st.title("Sign Language Classification")
+st.write("Upload an image of a hand sign (A-Y) and the model will predict the letter.")
+
+file = st.file_uploader('Select an image', type=['jpg', 'jpeg', 'png'])
+
+if file is not None:
+    img = Image.open(file)
+    st.image(img, caption='Uploaded Image', width=200)
+    
+    image = process_image(img)
+    prediction = model.predict(image)
+    predicted_class = np.argmax(prediction)
+    
+    class_names = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 
+                   'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 
+                   'V', 'W', 'X', 'Y']
+    
+    if predicted_class < len(class_names):
+        result = class_names[predicted_class]
+    else:
+        result = str(predicted_class)
+
+    st.write(f"Prediction: **{result}**")