Update app.py

app.py

@@ -1,7 +1,41 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+import numpy as np
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+import numpy as np
+import tensorflow as tf
+
+class TFLiteModel(tf.Module):
+    # ... (same as the previous TFLiteModel class definition)
+
+# Load the TFLite model
+model_path = 'path/to/your/tflite_model.tflite'
+interpreter = tf.lite.Interpreter(model_path=model_path)
+interpreter.allocate_tensors()
+
+# Load the input and output .npy files
+input_npy_file = 'input.npy'
+output_npy_file = 'output.npy'
+
+input_data = np.load(input_npy_file)
+output_data = np.load(output_npy_file)
+
+# Create the TFLiteModel instance
+model = TFLiteModel(interpreter)
+
+# Perform inference
+input_tensor = tf.convert_to_tensor(input_data, dtype=tf.float32)
+outputs = model(input_tensor)["outputs"]
+
+# Convert outputs to numpy array
+predicted_output = outputs.numpy()
+
+# Print the prediction and true output
+print("Predicted Output:")
+print(predicted_output)
+
+print("True Output:")
+print(output_data)
+
+iface = gr.Interface(fn=predict_sign_language, inputs="file", outputs="text")
+iface.launch(share=True)  # Set `share=True` to make it accessible outside the localhost