Update app.py

app.py

@@ -72,59 +72,72 @@ def process_and_predict(image):
 
 # Function to plot hand landmarks
 def plot_hand_landmarks(landmarks, title):
-    fig, ax = plt.subplots()
-    ax.scatter(landmarks[:, 0], landmarks[:, 1], c='blue')
+    fig, ax = plt.subplots(figsize=(10, 10))
+    ax.scatter(landmarks[:, 0], landmarks[:, 1], c='blue', s=50)
     for connection in mp.solutions.hands.HAND_CONNECTIONS:
         start_idx = connection[0]
         end_idx = connection[1]
         ax.plot([landmarks[start_idx, 0], landmarks[end_idx, 0]],
-                [landmarks[start_idx, 1], landmarks[end_idx, 1]], 'r-')
+                [landmarks[start_idx, 1], landmarks[end_idx, 1]], 'r-', linewidth=2)
     ax.invert_yaxis()
-    ax.set_title(title)
+    ax.set_title(title, fontsize=16)
     ax.axis('off')
     return fig
 
 # Streamlit app
+st.set_page_config(layout="wide")
 st.title("ASL Recognition App")
 
-# Create two columns
-col1, col2 = st.columns(2)
+# Create tabs for different functionalities
+tab1, tab2 = st.tabs(["Predict ASL Sign", "View Hand Landmarks"])
 
-with col1:
+with tab1:
     st.header("Predict ASL Sign")
     uploaded_file = st.file_uploader("Upload an image of an ASL sign", type=["jpg", "jpeg", "png"])
 
     if uploaded_file is not None:
-        image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), 1)
-        st.image(image, caption="Uploaded Image", use_column_width=True)
+        col1, col2 = st.columns(2)
+        with col1:
+            image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), 1)
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        with col2:
+            probabilities, landmarks = process_and_predict(image)
+            
+            if probabilities is not None:
+                st.subheader("Top 5 Predictions:")
+                top_indices = np.argsort(probabilities)[::-1][:5]
+                for i in top_indices:
+                    st.write(f"{model.classes_[i]}: {probabilities[i]:.2f}")
+                
+                fig = plot_hand_landmarks(landmarks, "Detected Hand Landmarks")
+                st.pyplot(fig)
+            else:
+                st.write("No hand detected in the image.")
 
-        probabilities, landmarks = process_and_predict(image)
-        
-        if probabilities is not None:
-            st.subheader("Top 5 Predictions:")
-            top_indices = np.argsort(probabilities)[::-1][:5]
-            for i in top_indices:
-                st.write(f"{model.classes_[i]}: {probabilities[i]:.2f}")
-        else:
-            st.write("No hand detected in the image.")
-
-with col2:
-    st.header("Draw Hand Landmarks")
+with tab2:
+    st.header("View Hand Landmarks")
     all_alphabets = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
     excluded_alphabets = 'DMNPTUVXZ'
     available_alphabets = ''.join(set(all_alphabets) - set(excluded_alphabets))
     
-    selected_alphabet = st.selectbox("Select an alphabet to draw landmarks:", list(available_alphabets))
-
-    if selected_alphabet:
-        image_path = f'asl test set/{selected_alphabet.lower()}.jpeg'
-        if os.path.exists(image_path):
-            image = cv2.imread(image_path)
-            _, landmarks = process_and_predict(image)
-            if landmarks is not None:
-                fig = plot_hand_landmarks(landmarks, f"Hand Landmarks for {selected_alphabet}")
-                st.pyplot(fig)
-            else:
-                st.write(f"No hand detected for {selected_alphabet}")
-        else:
-            st.write(f"Image not found for {selected_alphabet}")
+    selected_alphabets = st.multiselect("Select alphabets to view landmarks:", list(available_alphabets))
+
+    if selected_alphabets:
+        cols = st.columns(min(3, len(selected_alphabets)))
+        for idx, alphabet in enumerate(selected_alphabets):
+            with cols[idx % 3]:
+                image_path = f'asl test set/{alphabet.lower()}.jpeg'
+                if os.path.exists(image_path):
+                    image = cv2.imread(image_path)
+                    _, landmarks = process_and_predict(image)
+                    if landmarks is not None:
+                        fig = plot_hand_landmarks(landmarks, f"Hand Landmarks for {alphabet}")
+                        st.pyplot(fig)
+                    else:
+                        st.write(f"No hand detected for {alphabet}")
+                else:
+                    st.write(f"Image not found for {alphabet}")
+
+# Release MediaPipe resources
+hands.close()