Update app.py

app.py

@@ -7,7 +7,8 @@ import pandas as pd
 from numpy.linalg import norm
 import matplotlib.pyplot as plt
 import os
-st.set_page_config(layout="wide")
+import base64
+
 # Function to load the Random Forest model
 @st.cache_resource
 def load_model():
@@ -54,35 +55,21 @@ def calculate_angles(landmarks):
 
 # Function to process image and predict alphabet
 def process_and_predict(image):
-    if image is None:
-        st.error("Failed to load the image. Please check if the file exists and is not corrupted.")
-        return None, None
-
-    try:
-        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    except cv2.error:
-        st.error("Failed to convert the image. The image might be corrupted or in an unsupported format.")
-        return None, None
-
-    try:
-        results = hands.process(image_rgb)
-    except Exception as e:
-        st.error(f"An error occurred while processing the image: {str(e)}")
-        return None, None
-
-    if not results.multi_hand_landmarks:
-        st.warning("No hands detected in the image.")
-        return None, None
-
-    landmarks = np.array([[lm.x, lm.y] for lm in results.multi_hand_landmarks[0].landmark])
-    landmarks_normalized = normalize_landmarks(landmarks)
-    angles = calculate_angles(landmarks_normalized)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = hands.process(image_rgb)
     
-    angle_columns = [f'angle_{i}' for i in range(len(angles))]
-    angles_df = pd.DataFrame([angles], columns=angle_columns)
+    if results.multi_hand_landmarks:
+        landmarks = np.array([[lm.x, lm.y] for lm in results.multi_hand_landmarks[0].landmark])
+        landmarks_normalized = normalize_landmarks(landmarks)
+        angles = calculate_angles(landmarks_normalized)
+        
+        angle_columns = [f'angle_{i}' for i in range(len(angles))]
+        angles_df = pd.DataFrame([angles], columns=angle_columns)
+        
+        probabilities = model.predict_proba(angles_df)[0]
+        return probabilities, landmarks
     
-    probabilities = model.predict_proba(angles_df)[0]
-    return probabilities, landmarks
+    return None, None
 
 # Function to plot hand landmarks
 def plot_hand_landmarks(landmarks, title):
@@ -98,20 +85,24 @@ def plot_hand_landmarks(landmarks, title):
     ax.axis('off')
     return fig
 
-# Streamlit app
+# 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
 
+# Streamlit app
+st.set_page_config(layout="wide")
 st.title("ASL Recognition App")
 
-# Debug information
-st.write("Current working directory:", os.getcwd())
-image_directory = 'asl test set'
-st.write("Image directory path:", os.path.abspath(image_directory))
-
-if os.path.exists(image_directory):
-    image_files = os.listdir(image_directory)
-    st.write("Files in the image directory:", image_files)
-else:
-    st.error(f"The directory '{image_directory}' does not exist.")
+# 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)
 
 # Create tabs for different functionalities
 tab1, tab2 = st.tabs(["Predict ASL Sign", "View Hand Landmarks"])
@@ -121,22 +112,26 @@ with tab1:
     uploaded_file = st.file_uploader("Upload an image of an ASL sign", type=["jpg", "jpeg", "png"])
 
     if uploaded_file is not None:
-        col1, col2 = st.columns(2)
-        with col1:
+        try:
             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 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}")
+            if image is not None:
+                st.image(image, caption="Uploaded Image", use_column_width=True)
+                probabilities, landmarks = process_and_predict(image)
                 
-                fig = plot_hand_landmarks(landmarks, "Detected Hand Landmarks")
-                st.pyplot(fig)
+                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}")
+                    
+                    fig = plot_hand_landmarks(landmarks, "Detected Hand Landmarks")
+                    st.pyplot(fig)
+                else:
+                    st.write("No hand detected in the image.")
+            else:
+                st.error("Failed to load the image. The file might be corrupted.")
+        except Exception as e:
+            st.error(f"An error occurred while processing the image: {str(e)}")
 
 with tab2:
     st.header("View Hand Landmarks")
@@ -150,19 +145,22 @@ with tab2:
         cols = st.columns(min(3, len(selected_alphabets)))
         for idx, alphabet in enumerate(selected_alphabets):
             with cols[idx % 3]:
-                image_path = os.path.join(image_directory, f'{alphabet.lower()}.jpeg')
+                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):
-                    image = cv2.imread(image_path)
-                    if image is None:
-                        st.error(f"Failed to load image for {alphabet}. The file might be corrupted.")
-                        continue
-                    probabilities, 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.error(f"Failed to process image for {alphabet}")
+                    try:
+                        image = cv2.imread(image_path)
+                        if image is not None:
+                            probabilities, 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.error(f"No hand detected for {alphabet}")
+                        else:
+                            st.error(f"Failed to load image for {alphabet}. The file might be corrupted.")
+                    except Exception as e:
+                        st.error(f"An error occurred while processing image for {alphabet}: {str(e)}")
                 else:
                     st.error(f"Image not found for {alphabet}")