Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import matplotlib.pyplot as plt
+import pickle
+
+# Load the saved model
+model = load_model("ASL_CNN_model.h5")
+
+with open('label_list.pkl', 'rb') as f:
+    index_to_label = pickle.load(f)
+
+IMG_HEIGHT = 128
+IMG_WIDTH = 128
+
+def predict_sign_language(img):
+    # Preprocess the image
+    img = img.resize((IMG_HEIGHT, IMG_WIDTH))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array = img_array / 255.0  # Normalize if needed
+
+    # Make predictions
+    predictions = model.predict(img_array)
+    predicted_index = np.argmax(predictions[0])  # Get the index with the highest probability
+    predicted_label = index_to_label[predicted_index]
+
+    return predicted_label
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=predict_sign_language,
+    inputs=gr.Image(type="pil", label="Input Image"),  # Use 'pil' type to get a PIL image
+    outputs=gr.Textbox(label="Predicted Label"),
+    title="Sign Language Predictor",
+    description="<div style='text-align: center;'>Upload an image and the model will predict the sign language.</div>",
+    examples=[
+        ["L.png"],
+        ["lvb-cornuto5.webp"],
+        ["sign-language-hand-showing-c-260nw-740041291.webp"]
+    ]
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()