Rename src/streamlit_app.py to src/app.py

src/app.py

@@ -0,0 +1,64 @@
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# === CONFIG ===
+MODEL_PATH = 'trained_model/asl_model.h5'
+IMG_SIZE = 64
+CLASS_NAMES = [chr(i) for i in range(65, 91)]  # A-Z
+
+# Load model once
+@st.cache_resource(show_spinner=False)
+def load_model():
+    return tf.keras.models.load_model(MODEL_PATH)
+
+model = load_model()
+
+# === UI Header ===
+st.set_page_config(page_title="ASL Recognition", page_icon="🧠", layout="centered")
+st.markdown("<h1 style='text-align: center;'>🧠 ASL Alphabet Recognition</h1>", unsafe_allow_html=True)
+st.markdown("<p style='text-align: center;'>Upload a hand gesture image and get instant letter prediction.</p>", unsafe_allow_html=True)
+st.divider()
+
+# === Helper Functions ===
+def preprocess_image(image: Image.Image):
+    img = image.convert("RGB")
+    img = img.resize((IMG_SIZE, IMG_SIZE))
+    img = np.array(img) / 255.0
+    img = np.expand_dims(img, axis=0)
+    return img
+
+def predict(img: Image.Image):
+    processed = preprocess_image(img)
+    preds = model.predict(processed)
+    class_idx = np.argmax(preds)
+    confidence = preds[0][class_idx]
+    return CLASS_NAMES[class_idx], confidence
+
+# === Upload UI ===
+uploaded_file = st.file_uploader("📁 Upload a hand gesture image", type=['png', 'jpg', 'jpeg'])
+
+if uploaded_file:
+    col1, col2 = st.columns([1, 2])
+    with col1:
+        img = Image.open(uploaded_file)
+        st.image(img, caption="📷 Uploaded Image", use_column_width=True)
+    with col2:
+        st.write("### 🔍 Prediction")
+        label, confidence = predict(img)
+        st.success(f"Predicted Letter: **:blue[{label}]**")
+        st.metric(label="Confidence Score", value=f"{confidence * 100:.2f}%", delta=None)
+
+        # Optional: show full probabilities as a horizontal bar chart
+        preds = model.predict(preprocess_image(img))[0]
+        top_indices = np.argsort(preds)[::-1][:5]
+        st.write("#### 🔢 Top 5 Predictions")
+        for i in top_indices:
+            st.progress(float(preds[i]), text=f"{CLASS_NAMES[i]}: {preds[i]*100:.2f}%")
+else:
+    st.info("📸 Upload a clear image showing a single hand gesture on a plain background.")
+
+# === Footer ===
+st.divider()
+st.markdown("<small style='text-align:center; display:block;'>Developed with ❤️ using TensorFlow & Streamlit</small>", unsafe_allow_html=True)

src/streamlit_app.py

@@ -1,40 +0,0 @@
-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])),
-    ))