Create app.py

app.py

@@ -0,0 +1,63 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
+
+
+title = "Code Generator"
+description = "This is a space to convert english text to Python code using with [codeparrot-small-text-to-code](https://huggingface.co/codeparrot/codeparrot-small-text-to-code),\
+            a code generation model for Python finetuned on [github-jupyter-text](https://huggingface.co/datasets/codeparrot/github-jupyter-text) a dataset of doctrings\
+            and their Python code extracted from Jupyter notebooks."
+example = [
+    ["Utility function to compute the accuracy of predictions using metric from sklearn", 65, 0.6, 42],
+    ["Let's implement a function that computes the size of a file called filepath", 60, 0.6, 42],
+    ["Let's implement bubble sort in a helper function:", 87, 0.6, 42],
+    ]
+
+# change model to the finetuned one
+tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-text-to-code")
+model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot-small-text-to-code")
+
+def make_doctring(gen_prompt):
+    return "\"\"\"\n" + gen_prompt + "\n\"\"\"\n\n"
+
+def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42):
+    set_seed(seed)
+    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    prompt = make_doctring(gen_prompt)
+    generated_text = pipe(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.Code(lines=10, language="python", label="English instructions"),
+        gr.inputs.Slider(
+            minimum=8,
+            maximum=256,
+            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.Code(label="Predicted Python code", language="python", lines=10),
+    examples=example,
+    layout="horizontal",
+    theme="peach",
+    description=description,
+    title=title
+)
+iface.launch()