Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("sergeantson/GPT2_Medium_Law")
+model = AutoModelForCausalLM.from_pretrained("sergeantson/GPT2_Medium_Law")
+
+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=False)