Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "sambanovasystems/SambaNova-Qwen2.5-Coder-Artifacts"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_text(prompt):
+    # Generate text using the model
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
+    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return text
+
+# Build the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='text-align: center;'>AI Text Generator</h1>")
+    with gr.Row():
+        with gr.Column():
+            user_input = gr.Textbox(label="Input Prompt", placeholder="Enter your prompt here...")
+            generate_btn = gr.Button("Generate Text")
+        with gr.Column():
+            output_text = gr.Textbox(label="Generated Text", readonly=True)
+    generate_btn.click(generate_text, inputs=user_input, outputs=output_text)
+
+# Launch the app
+demo.launch(share=True, theme="macos", css=".gradio-container {background-color: #f0f0f0;}")