Update app.py

app.py

@@ -7,9 +7,14 @@ import pandas as pd
 from numpy.linalg import norm
 import matplotlib.pyplot as plt
 import os
-import base64
 
 st.set_page_config(layout="wide")
+
+# Define the alphabets
+all_alphabets = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
+excluded_alphabets = 'DMNPTUVXZ'
+working_alphabets = ''.join(set(all_alphabets) - set(excluded_alphabets))
+
 # Function to load the Random Forest model
 @st.cache_resource
 def load_model():
@@ -67,37 +72,49 @@ def process_and_predict(image):
 
 # Function to plot hand landmarks
 def plot_hand_landmarks(landmarks, title):
-    fig, ax = plt.subplots(figsize=(10, 10))
-    ax.scatter(landmarks[:, 0], landmarks[:, 1], c='blue', s=50)
+    fig, ax = plt.subplots(figsize=(5, 5))
+    ax.scatter(landmarks[:, 0], landmarks[:, 1], c='blue', s=20)
     mp_hands = mp.solutions.hands
     for connection in mp_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-', linewidth=2)
+                [landmarks[start_idx, 1], landmarks[end_idx, 1]], 'r-', linewidth=1)
     ax.invert_yaxis()
-    ax.set_title(title, fontsize=16)
+    ax.set_title(title, fontsize=12)
     ax.axis('off')
     return fig
 
-# Function to create a download link for the README file
-def get_binary_file_downloader_html(bin_file, file_label='File'):
-    with open(bin_file, 'rb') as f:
-        data = f.read()
-    bin_str = base64.b64encode(data).decode()
-    href = f'<a href="data:application/octet-stream;base64,{bin_str}" download="{os.path.basename(bin_file)}">Download {file_label}</a>'
-    return href
+# README content
+readme_content = f"""
+## How it works
 
-# Streamlit app
+This ASL Recognition App uses image processing and machine learning to recognize American Sign Language (ASL) hand signs.
+
+1. **Image Upload**: Users can upload an image of an ASL hand sign.
+2. **Hand Detection**: The app uses MediaPipe to detect hand landmarks in the image.
+3. **Feature Extraction**: Angles between hand landmarks are calculated and normalized.
+4. **Prediction**: A Random Forest model predicts the ASL sign based on the extracted features.
+5. **Visualization**: The app displays the detected hand landmarks and top predictions.
+
+### Supported Alphabets
+
+The app currently works for the following ASL alphabets:
+{', '.join(working_alphabets)}
 
+The app does not support or may not work correctly for:
+{', '.join(excluded_alphabets)}
+
+Note: The model's performance may vary and is subject to improvement.
+
+The "View Hand Landmarks" tab allows users to see hand landmarks for pre-loaded ASL signs.
+"""
+
+# Streamlit app
 st.title("ASL Recognition App")
 
-# Add README button
-readme_col1, readme_col2 = st.columns([1, 3])
-with readme_col1:
-    st.markdown("## How it works")
-with readme_col2:
-    st.markdown(get_binary_file_downloader_html('readme.md', 'README'), unsafe_allow_html=True)
+# Display README content
+st.sidebar.markdown(readme_content)
 
 # Create tabs for different functionalities
 tab1, tab2 = st.tabs(["Predict ASL Sign", "View Hand Landmarks"])
@@ -110,14 +127,18 @@ with tab1:
         try:
             image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), 1)
             if image is not None:
-                st.image(image, caption="Uploaded Image", use_column_width=True)
+                col1, col2 = st.columns(2)
+                with col1:
+                    st.image(image, caption="Uploaded Image", use_column_width=True)
+                
                 probabilities, landmarks = process_and_predict(image)
                 
                 if probabilities is not None and landmarks 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}")
+                    with col2:
+                        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)
@@ -130,18 +151,14 @@ with tab1:
 
 with tab2:
     st.header("View Hand Landmarks")
-    all_alphabets = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
-    excluded_alphabets = 'DMNPTUVXZ'
-    available_alphabets = ''.join(set(all_alphabets) - set(excluded_alphabets))
     
-    selected_alphabets = st.multiselect("Select alphabets to view landmarks:", list(available_alphabets))
+    selected_alphabets = st.multiselect("Select alphabets to view landmarks:", list(working_alphabets))
 
     if selected_alphabets:
-        cols = st.columns(min(3, len(selected_alphabets)))
+        cols = st.columns(4)  # 4 columns for smaller images
         for idx, alphabet in enumerate(selected_alphabets):
-            with cols[idx % 3]:
+            with cols[idx % 4]:
                 image_path = os.path.join('asl test set', f'{alphabet.lower()}.jpeg')
-                st.write(f"Attempting to load: {image_path}")
                 if os.path.exists(image_path):
                     try:
                         image = cv2.imread(image_path)
@@ -153,8 +170,8 @@ with tab2:
                             else:
                                 st.error(f"No hand detected for {alphabet}")
                         else:
-                            st.error(f"Failed to load image for {alphabet}. The file might be corrupted.")
+                            st.error(f"Failed to load image for {alphabet}")
                     except Exception as e:
-                        st.error(f"An error occurred while processing image for {alphabet}: {str(e)}")
+                        st.error(f"Error processing image for {alphabet}")
                 else:
                     st.error(f"Image not found for {alphabet}")