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Update app.py
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app.py
CHANGED
@@ -2,28 +2,45 @@ import gradio as gr
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load
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MODEL_NAME = "ubiodee/Cardano_plutus"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
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#
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def generate_response(prompt):
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inputs = tokenizer(prompt, return_tensors="pt")
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with torch.no_grad():
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return response
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# Gradio
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fn=generate_response,
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inputs=gr.Textbox(label="Enter your prompt"),
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outputs=gr.Textbox(label="Model Response"),
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title="Cardano Plutus AI",
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description="
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theme="default"
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)
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iface.launch()
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load model & tokenizer
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MODEL_NAME = "ubiodee/Cardano_plutus"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
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model.eval()
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if torch.cuda.is_available():
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model.to("cuda")
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# Response function
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def generate_response(prompt):
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=200,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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# Remove the prompt from the output to return only the answer
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if response.startswith(prompt):
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response = response[len(prompt):].strip()
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return response
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# Gradio UI
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demo = gr.Interface(
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fn=generate_response,
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inputs=gr.Textbox(label="Enter your prompt", lines=4, placeholder="Ask about Plutus..."),
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outputs=gr.Textbox(label="Model Response"),
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title="Cardano Plutus AI Assistant",
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description="Ask questions about Plutus smart contracts or Cardano blockchain."
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)
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demo.launch()
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