Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
+from transformers import pipeline
+import os
+
+description = """# SantaCoder Endpoint"""
+token = os.environ["HUB_TOKEN"]
+device="cuda:0"
+
+tokenizer = AutoTokenizer.from_pretrained("bigcode/christmas-models", use_auth_token=token)
+model = AutoModelForCausalLM.from_pretrained("bigcode/christmas-models", 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
+
+
+demo = gr.Blocks()
+with demo:
+    with gr.Row():
+        gr.Markdown(value=description)
+    with gr.Row():
+        with gr.Column():
+            code = gr.Textbox(lines=10, label="Input code")
+            max_tokens= gr.Slider(
+                minimum=8,
+                maximum=1000,
+                step=1,
+                label="Number of tokens to generate",
+            )
+            temperature = gr.Slider(
+                minimum=0.1,
+                maximum=2.5,
+                step=0.1,
+                label="Temperature",
+            )
+            seed = gr.Slider(
+                minimum=0,
+                maximum=1000,
+                step=1,
+                label="Random seed to use for the generation"
+            )
+            run = gr.Button()
+        with gr.Column():
+            output = gr.Textbox(lines=10, label="Generated code")
+
+    event = run.click(code_generation, [code, max_tokens, temperature, seed], output)
+    gr.HTML(label="Contact", value="<img src='https://huggingface.co/datasets/bigcode/admin/resolve/main/bigcode_contact.png' alt='contact' style='display: block; margin: auto; max-width: 800px;'>")
+
+demo.launch()