Update app.py

app.py

@@ -1,39 +1,5 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Load the tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("sergeantson/GPT2_Large_Law")
-model = AutoModelForCausalLM.from_pretrained("sergeantson/GPT2_Large_Law")
+demo = gr.load("sergeantson/GPT2_Large_Law", src="models")
 
-def generate_text(input_text, max_length, num_return_sequences, temperature, top_k, top_p):
-    inputs = tokenizer(input_text, return_tensors="pt")
-    output = model.generate(
-        **inputs,
-        max_length=max_length,
-        num_return_sequences=num_return_sequences,
-        temperature=temperature,
-        top_k=top_k,
-        top_p=top_p,
-        no_repeat_ngram_size=2  # Prevents repeating n-grams
-    )
-    generated_texts = [tokenizer.decode(output[i], skip_special_tokens=True) for i in range(num_return_sequences)]
-    return "\n\n".join(generated_texts)
-
-# Set up the Gradio interface
-iface = gr.Interface(
-    fn=generate_text,
-    inputs=[
-        gr.Textbox(lines=2, placeholder="Enter a prompt here...", label="Input Text"),
-        gr.Slider(minimum=10, maximum=200, value=50, step=1, label="Max Length"),
-        gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of Return Sequences"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(minimum=1, maximum=100, value=50, step=1, label="Top-k"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.1, label="Top-p")
-    ],
-    outputs="text",
-    title="Legal Text Generator",
-    description="Enter a prompt to generate legal text based on the input."
-)
-
-# Launch the interface
-iface.launch(share=True)
+demo.launch()