update files

README.md

@@ -1,10 +1,10 @@
 ---
 title: Python To Text
-emoji: 🔥
-colorFrom: yellow
-colorTo: blue
+emoji: 🪞
+colorFrom: red
+colorTo: purple
 sdk: gradio
-sdk_version: 3.0.26
+sdk_version: 3.0.24
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -0,0 +1,62 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
+
+
+title = "Python to Text Converter [WIP]"
+description = "This is a space to convert Python code into english text explaining what it does using [codeparrot-small-code-to-text](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-text) a dataset Python code followed by a doctring explaining it, the data was extracted from Jupyter notebooks."
+example = [
+    ["example1", 65, 0.6, 42],
+    ["example2", 60, 0.6, 42],
+    ["example3", 87, 0.6, 42],
+    ]
+
+# change model to the finetuned one
+tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small")
+model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot-small")
+
+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)
+    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.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",
+    theme="peach",
+    description=description,
+    title=title
+)
+iface.launch()

requirements.txt

@@ -0,0 +1,2 @@
+transformers==4.19.0
+torch==1.11.0