Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.text import tokenizer_from_json
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+# Load the trained model
+model = load_model("text_to_wingdings_model_complex.h5")
+
+# Load the tokenizer
+with open("tokenizer.json") as json_file:
+    tokenizer = tokenizer_from_json(json_file.read())
+
+# Function to convert text to Wingdings
+def convert_to_wingdings(input_text):
+    # Preprocess the input text
+    text_sequence = tokenizer.texts_to_sequences([input_text])
+    max_length = 500  # Set to 500 as desired
+    text_sequence = pad_sequences(text_sequence, maxlen=max_length, padding='post')
+
+    # Predict the output
+    predictions = model.predict(text_sequence)
+    wingdings_sequence = np.argmax(predictions, axis=-1)
+
+    # Convert the sequence back to characters
+    wingdings_output = ''.join([tokenizer.index_word[i] for i in wingdings_sequence[0] if i != 0])
+    
+    return wingdings_output
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=convert_to_wingdings,
+    inputs=gr.Textbox(label="Input Text", placeholder="Type your text here..."),
+    outputs=gr.Textbox(label="Wingdings Output"),
+    title="Text to Wingdings Converter",
+    description="Enter text to convert it to Wingdings."
+)
+
+# Launch the interface
+iface.launch()