Create app.py

app.py

@@ -0,0 +1,26 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Define the prediction function
+def predict(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+    outputs=gr.outputs.Textbox(label="Generated Text"),
+    title="DeepSeek-R1-Distill-Qwen-1.5B Text Generation",
+    description="Enter text and the model will generate a continuation.",
+)
+
+if __name__ == "__main__":
+    iface.launch()