Update app.py

app.py

@@ -2,62 +2,42 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
 
 
-title = "Code Explainer"
-description = "This is a space to convert Python code into english text explaining what it does using [codeparrot-small-code-to-text](https://huggingface.co/codeparrot/codeparrot-small-code-to-text),\
-            a code generation model for Python finetuned on [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text) a dataset of Python code followed by a docstring explaining it, the data was originally extracted from Jupyter notebooks."
-
-EXAMPLE_1 = "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_2 =  "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_3 = "def load_text(file)\n    with open(filename, 'r') as f:\n        text = f.read()\n    return text"
-example = [
-    [EXAMPLE_1, 32, 0.6, 42],
-    [EXAMPLE_2, 16, 0.6, 42],
-    [EXAMPLE_3, 11, 0.2, 42],
-    ]
-
-# change model to the finetuned one
 tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-code-to-text")
 model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot-small-code-to-text")
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, num_return_sequences=1, device=0)
+
 
 def make_doctring(gen_prompt):
     return gen_prompt + f"\n\n\"\"\"\nExplanation:"
 
-def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42):
+
+def code_generation(gen_prompts, max_tokens=8, 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
+    prompts = [make_doctring(p) for p in gen_prompts]
+    generated_text = pipe(prompts, do_sample=True, top_p=0.95, temperature=temperature, max_length=max_tokens)[0]
+    return generated_text["generated_text"]
+
 
+title = "Code Explainer"
+description = "This is a space to convert Python code into english text explaining what it does using [codeparrot-small-code-to-text](https://huggingface.co/codeparrot/codeparrot-small-code-to-text),\
+            a code generation model for Python finetuned on [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text) a dataset of Python code followed by a docstring explaining it, the data was originally extracted from Jupyter notebooks."
 
+EXAMPLES = [
+    ["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]"],
+    ["from sklearn import model_selection\nX_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=0.2)"],
+    ["def load_text(filename):\n    with open(filename, 'r') as f:\n        text = f.read()\n    return text"]
+]
+ 
 iface = gr.Interface(
-    fn=code_generation, 
+    fn=code_generation,
     inputs=[
-        gr.Code(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"
-        )
+        gr.inputs.Code(language="python", label="Python code snippet", lines=10),
+        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")
     ],
-    outputs=gr.Code(label="Predicted explanation", lines=10),
-    examples=example,
+    outputs=gr.outputs.Code(language="text", label="Generated explanation", lines=10),
+    examples=EXAMPLES,
     layout="horizontal",
     theme="peach",
     description=description,