Create app.py

app.py

@@ -0,0 +1,48 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+# Load the tokenizer and model
+model_path = 'nvidia/Minitron-4B-Base'
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map='cpu', torch_dtype=torch.float32)
+
+def generate_text(prompt, max_length=100):
+    # Encode the input text
+    inputs = tokenizer.encode(prompt, return_tensors='pt')
+    
+    # Generate the output
+    outputs = model.generate(
+        inputs,
+        max_length=max_length,
+        num_return_sequences=1,
+        no_repeat_ngram_size=2,
+        do_sample=True,
+        temperature=0.7,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    # Decode and return the output
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="Enter your prompt", placeholder="Type your prompt here..."),
+        gr.Slider(minimum=20, maximum=200, value=100, step=10, label="Max Length")
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="Text Generation with Minitron-4B",
+    description="Enter a prompt and get AI-generated text completion.",
+    examples=[
+        ["Complete the paragraph: our solar system is"],
+        ["Write a short story about"],
+        ["Explain the concept of"]
+    ]
+)
+
+# Launch the application
+if __name__ == "__main__":
+    demo.launch(share=False)