Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model & tokenizer
+MODEL_NAME = "ubiodee/Test_Plutus"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+model.eval()
+
+if torch.cuda.is_available():
+    model.to("cuda")
+
+# Response function
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+        )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Remove the prompt from the output to return only the answer
+    if response.startswith(prompt):
+        response = response[len(prompt):].strip()
+
+    return response
+
+# Gradio UI
+demo = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(label="Enter your prompt", lines=4, placeholder="Ask about Plutus..."),
+    outputs=gr.Textbox(label="Model Response"),
+    title="Cardano Plutus AI Assistant",
+    description="Write Plutus smart contracts on Cardano blockchain."
+)
+
+demo.launch()