Create app.py

app.py

@@ -0,0 +1,33 @@
+import json
+import torch
+import gradio as gr
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load the fine-tuned model
+model_name = "./t5-finetuned-final"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# Move model to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Define the function for inference
+def generate_command(input_command):
+    prompt = "extract: " + input_command
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+    output_ids = model.generate(input_ids, max_length=128, num_beams=5, early_stopping=True)
+    return tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_command,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a command..."),
+    outputs=gr.Textbox(label="Extracted JSON Output"),
+    title="T5 Fine-Tuned Command Extractor",
+    description="Enter a command, and the fine-tuned T5 model will extract relevant details in JSON format.",
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()