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README.md

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 ---
-title: Sign Language
-emoji: 📊
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 5.20.1
-app_file: app.py
+title: Sign Language Detection
+emoji: 💻
+colorFrom: pink
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.36.0
+app_file: prototype_static_test.py
 pinned: false
+short_description: Peforms ASL Sign Language Prediction
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

gitattributes

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mlp_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:804899867625543de815a28c878b7a4c1333a57a4f0994c9f5f87baa468ace91
+size 255840

packages.txt

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+libgl1

prototype_static_test.py

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+import streamlit as st
+import cv2
+import numpy as np
+import joblib
+import mediapipe as mp
+from PIL import Image
+import os
+import tempfile
+
+# Load the trained MLP Classifier model
+model = joblib.load('mlp_model.joblib')
+
+# Loading the class dictionary
+class_names = {i: str(i) for i in range(10)}  # For numbers 0-9
+class_names.update({10 + i: chr(97 + i) for i in range(26)})  # For letters a-z
+
+# Initialize MediaPipe Hand model
+mp_hands = mp.solutions.hands
+hands = mp_hands.Hands(static_image_mode=True, max_num_hands=1, min_detection_confidence=0.5)
+
+
+def skeletal_image(image_path, shape=(256, 256, 3)):
+    image = cv2.imread(image_path)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = hands.process(image_rgb) 
+    if not results.multi_hand_landmarks:
+        return None
+    white_background = np.ones(shape, dtype=np.uint8) * 255
+    for hand_landmarks in results.multi_hand_landmarks:
+        mp.solutions.drawing_utils.draw_landmarks(white_background, hand_landmarks, mp_hands.HAND_CONNECTIONS)
+    white_background_bgr = cv2.cvtColor(white_background, cv2.COLOR_RGB2BGR)
+    return white_background_bgr
+
+
+# Function to extract landmarks from an uploaded image
+def extract_landmarks(image_path):
+    image = cv2.imread(image_path)
+    results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    if results.multi_hand_landmarks:
+        # Extract landmarks
+        landmarks = np.array([[lm.x, lm.y, lm.z] for lm in results.multi_hand_landmarks[0].landmark]).flatten()
+        return landmarks
+    return None
+
+
+
+# Streamlit app
+st.title('Hand Gesture Recognition')
+
+# Option for the user to choose the input method
+input_method = st.radio("Choose the input method:", ("Upload an Image", "Use Webcam"))
+
+if input_method == "Upload an Image":
+    uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        uploaded_image = Image.open(uploaded_file).convert('RGB')
+elif input_method == "Use Webcam":
+    uploaded_file = st.camera_input("Take a picture")
+    if uploaded_file is not None:
+        uploaded_image = Image.open(uploaded_file).convert('RGB')
+
+if uploaded_file is not None:
+    st.image(uploaded_image, caption='Uploaded Image', use_column_width=True)
+    
+    # Save the uploaded or captured image to a temporary file
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmpfile:
+        uploaded_image.save(tmpfile, format="JPEG")
+        tmpfile_path = tmpfile.name
+    
+    try:
+        skeletal_img = skeletal_image(tmpfile_path)
+        if skeletal_img is not None:
+            st.image(skeletal_img, channels="BGR", caption='This processed image contains your hand landmarks')
+        processed_image = extract_landmarks(tmpfile_path)
+        
+        if processed_image is not None:
+            with st.spinner('Please wait, while the model predicts...'):
+                # Reshape the processed_image for the model if necessary
+                processed_image = processed_image.reshape(1, -1)  # Reshape if your model expects a specific input shape
+                predictions = model.predict(processed_image)
+                predicted_class_name = class_names[predictions[0]]
+                
+                # Display the prediction
+                st.write(f"The predicted ASL sign seems to be {predicted_class_name.upper()}")
+        else:
+            st.write("No hand landmarks were detected.")
+    finally:
+        # Ensure the temporary file is deleted even if an error occurs
+        os.remove(tmpfile_path)
+
+

requirements.txt

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+mediapipe
+scikit-learn
+numpy==1.26.4
+opencv-python
+tensorflow
+joblib
+Pillow
+matplotlib
+pandas
+seaborn