Duplicate from bigcode/santa-explains-code

Co-authored-by: Loubna Ben Allal <[email protected]>

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README.md

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+---
+title: Santa Explains Code
+emoji: 🎅
+colorFrom: red
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.0.24
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: bigcode/santa-explains-code
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
+
+
+title = "🎅 Santa Explains Code"
+description = "This space converts Python code into English text that explains its function using [SantaCoder-Code-To-Text](https://huggingface.co/loubnabnl/santacoder-code-to-text), \
+a code generation model that was fine-tuned on the [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text) dataset. \
+This dataset includes Python code accompanied by docstrings that explain it. The data was sourced from Jupyter notebooks.\n\n\
+Limitations: The model was fine-tuned on a small dataset from Jupyter Notebooks, so it can only explain simple, \
+common functions that are found in these notebooks, in a similar fashion to the text in markdown cells. It might also be sensitive to function names and comments."
+
+EXAMPLE_0 = "def function(sequence):\n    return [x for x in sequence if x % 2 == 0]"
+EXAMPLE_1 =  "from sklearn import model_selection\nX_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=0.2)"
+EXAMPLE_2 = "def load_text(file)\n    with open(filename, 'r') as f:\n        text = f.read()\n    return text"
+EXAMPLE_3 = "net.zero_grad()\nloss.backward()"
+EXAMPLE_4 = "net.zero_grad()\nloss.backward()\n\nnoptimizer.step()"
+EXAMPLE_5 = "def sort_function(arr):\n    n = len(arr)\n \n    # Traverse through all array elements\n    for i in range(n):\n \n        # Last i elements are already in place\n        for j in range(0, n-i-1):\n \n            # traverse the array from 0 to n-i-1\n            # Swap if the element found is greater\n            # than the next element\n            if arr[j] > arr[j+1]:\n                arr[j], arr[j+1] = arr[j+1], arr[j]"
+
+example = [
+    [EXAMPLE_0, 32, 0.6, 42],
+    [EXAMPLE_1, 34, 0.4, 42],
+    [EXAMPLE_2, 11, 0.6, 42],
+    [EXAMPLE_3, 30, 0.6, 42],
+    [EXAMPLE_4, 46, 0.6, 42],
+    [EXAMPLE_5, 32, 0.6, 42],
+    ]
+
+
+tokenizer = AutoTokenizer.from_pretrained("loubnabnl/santacoder-code-to-text")
+model = AutoModelForCausalLM.from_pretrained("loubnabnl/santacoder-code-to-text", trust_remote_code=True)
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+def make_doctring(gen_prompt):
+    return gen_prompt + f"\n\n\"\"\"\nExplanation:"
+
+def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42):
+    set_seed(seed)
+    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.Textbox(lines=10, label="Python code"),
+        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.Textbox(label="Predicted explanation", lines=10),
+    examples=example,
+    layout="horizontal",
+    description=description,
+    title=title
+)
+iface.launch()

requirements.txt

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+transformers==4.19.0
+torch==1.11.0