Create App.py

App.py

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+import gradio as gr
+import tensorflow as tf
+from transformers import BertTokenizer, TFBertModel
+import numpy as np
+
+# Load your model
+model = tf.keras.models.load_model('models/model_files')
+
+# Load tokenizer
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+
+def preprocess_text(text):
+    inputs = tokenizer(text, return_tensors='tf', padding=True, truncation=True, max_length=512)
+    return inputs
+
+def predict(text, image, structured):
+    text_inputs = preprocess_text(text)
+    image = tf.image.resize(image, (224, 224))
+    image = tf.keras.applications.resnet50.preprocess_input(image)
+    structured = (structured - structured.mean()) / structured.std()
+    prediction = model.predict([text_inputs['input_ids'], text_inputs['attention_mask'], image, structured])
+    return prediction[0][0]
+
+# Define the chat function
+def chat_response(user_input):
+    return f"Model response to: {user_input}"
+
+# Define the code execution function
+def execute_code(code):
+    exec_globals = {}
+    exec(code, exec_globals)
+    return exec_globals.get("output", "No output")
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            chat_input = gr.Textbox(lines=2, placeholder="Enter your message here...")
+            chat_output = gr.Textbox(lines=5, placeholder="Model response will appear here...")
+            chat_button = gr.Button("Send")
+        with gr.Column():
+            code_input = gr.Textbox(lines=10, placeholder="Enter your code here...")
+            code_output = gr.Textbox(lines=5, placeholder="Code output will appear here...")
+            code_button = gr.Button("Run Code")
+    
+    chat_button.click(chat_response, inputs=chat_input, outputs=chat_output)
+    code_button.click(execute_code, inputs=code_input, outputs=code_output)
+
+demo.launch()