Create app.py

app.py

@@ -0,0 +1,61 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
+from transformers import pipeline
+
+
+title = "Santa Model Generator"
+description = "Demo"
+example = [
+    ["def print_hello_world():", 8, 0.6, 42],
+    ["def get_file_size(filepath):", 24, 0.6, 42],
+    ["def count_lines(filename):", 40, 0.6, 42],
+    ["def count_words(filename):", 40, 0.6, 42]]
+
+token = os.environ["HUB_TOKEN"]
+device="cpu" #"cuda:0"
+
+tokenizer = AutoTokenizer.from_pretrained("bigcode/christmas-models", use_auth_token=token)
+model = AutoModelForCausalLM.from_pretrained("bigcode/christmas-models", revision="dedeup-alt", trust_remote_code=True, use_auth_token=token)
+
+
+def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42):
+    set_seed(seed)
+    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    generated_text = pipe(gen_prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text']
+    return generated_text
+
+
+iface = gr.Interface(
+    fn=code_generation, 
+    inputs=[
+        gr.Textbox(lines=10, label="Input code"),
+        gr.inputs.Slider(
+            minimum=8,
+            maximum=1000,
+            step=1,
+            default=8,
+            label="Number of tokens to generate",
+        ),
+        gr.inputs.Slider(
+            minimum=0,
+            maximum=2.5,
+            step=0.1,
+            default=0.6,
+            label="Temperature",
+        ),
+        gr.inputs.Slider(
+            minimum=0,
+            maximum=1000,
+            step=1,
+            default=42,
+            label="Random seed to use for the generation"
+        )
+    ],
+    outputs=gr.Textbox(label="Predicted code", lines=10),
+    examples=example,
+    layout="horizontal",
+    theme="peach",
+    description=description,
+    title=title
+)
+iface.launch()