add app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
+from transformers import pipeline
+
+
+title = "CodeParrot Generator 🦜"
+description = "This is a subspace to make code generation with CodeParrot, it is used in a larger space for model comparison."
+examples = "def print_hello_world():\n \"""Print 'Hello world' \""" "
+tokenizer = load_tokenizer("lvwerra/codeparrot")
+model = load_model("lvwerra/codeparrot")
+    
+
+def code_generation(gen_prompt, strategy, max_tokens, seed=42):
+    set_seed(seed)
+    gen_kwargs = {}
+    gen_kwargs["do_sample"] = strategy == "Sample"
+    gen_kwargs["max_new_tokens"] = max_tokens
+    if gen_kwargs["do_sample"]:
+        gen_kwargs["temperature"] = 0.2
+        gen_kwargs["top_k"] = 0
+        gen_kwargs["top_p"] = 0.95
+    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    generated_text = pipe(gen_prompt, **gen_kwargs)[0]['generated_text']
+    return generated_text
+
+
+interface = gr.Interface(
+    fn=code_generation, 
+    inputs=[
+        gr.Textbox(lines=10, label="Input code"),
+        gr.Dropdown(choices=["Greedy", "Sample"], value="Greedy"),
+        gr.inputs.Slider(
+            minimum=8,
+            maximum=256,
+            step=1,
+            default=8,
+            label="Number of tokens to generate",
+        ),
+        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=examples,
+    layout="horizontal",
+    theme="peach",
+    description=description,
+    title=title
+)
+interface.launch()