Update app.py

app.py

@@ -53,8 +53,7 @@ def calculate_angles(landmarks):
     return angles
 
 # Function to process image and predict alphabet
-def process_and_predict(image_path):
-    image = cv2.imread(image_path)
+def process_and_predict(image):
     image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     results = hands.process(image_rgb)
     
@@ -67,11 +66,9 @@ def process_and_predict(image_path):
         angles_df = pd.DataFrame([angles], columns=angle_columns)
         
         probabilities = model.predict_proba(angles_df)[0]
-        predicted_class = model.classes_[np.argmax(probabilities)]
-        
-        return predicted_class, probabilities, landmarks
+        return probabilities, landmarks
     
-    return None, None, None
+    return None, None
 
 # Function to plot hand landmarks
 def plot_hand_landmarks(landmarks, title):
@@ -90,38 +87,44 @@ def plot_hand_landmarks(landmarks, title):
 # Streamlit app
 st.title("ASL Recognition App")
 
-# Define available alphabets
-all_alphabets = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
-excluded_alphabets = 'DMNPTUVXZ'
-available_alphabets = ''.join(set(all_alphabets) - set(excluded_alphabets))
+# Create two columns
+col1, col2 = st.columns(2)
 
-# Multi-select for alphabets
-selected_alphabets = st.multiselect("Select alphabets to recognize:", list(available_alphabets))
+with col1:
+    st.header("Predict ASL Sign")
+    uploaded_file = st.file_uploader("Upload an image of an ASL sign", type=["jpg", "jpeg", "png"])
 
-if selected_alphabets:
-    num_selected = len(selected_alphabets)
-    cols = min(3, num_selected)  # Maximum 3 columns
-    rows = (num_selected + cols - 1) // cols
-    
-    for i in range(0, num_selected, cols):
-        row_alphabets = selected_alphabets[i:i+cols]
-        cols_in_row = st.columns(len(row_alphabets))
+    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)
+
+        probabilities, landmarks = process_and_predict(image)
         
-        for j, alphabet in enumerate(row_alphabets):
-            with cols_in_row[j]:
-                image_path = f'asl test set/{alphabet}.jpeg'
-                if os.path.exists(image_path):
-                    predicted_class, probabilities, landmarks = process_and_predict(image_path)
-                    if predicted_class is not None:
-                        st.write(f"Alphabet: {alphabet}")
-                        st.write(f"Predicted: {predicted_class}")
-                        st.write(f"Confidence: {probabilities[model.classes_.tolist().index(predicted_class)]:.2f}")
-                        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}")
-
-else:
-    st.write("Please select at least one alphabet to recognize.")
+        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")
+    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}")