Updated app.py to use Hugging Face inference

app.py

@@ -1,23 +1,17 @@
 import gradio as gr
-from tensorflow.keras.models import load_model
-import json
+from huggingface_hub import InferenceApi
 
-# Load model and tokenizer
-model = load_model('my_model.h5')
+# Use Hugging Face API to load the model (replace "willco-afk/languages" with your repo name)
+inference = InferenceApi(repo_id="willco-afk/languages")
 
-with open('tokenizer.json', 'r') as f:
-    tokenizer = json.load(f)
+# Define the prediction function
+def predict(input_text):
+    # Call the model on Hugging Face
+    result = inference(input_text)
+    return result
 
-# Define prediction function
-def predict(text):
-    # Implement your tokenization and model prediction logic here
-    # Example:
-    tokens = tokenizer.texts_to_sequences([text])
-    prediction = model.predict(tokens)
-    return prediction
-
-# Set up Gradio interface
+# Define the Gradio interface
 iface = gr.Interface(fn=predict, inputs="text", outputs="text")
 
-# Launch the interface
+# Launch the interface locally (optional)
 iface.launch()