initial commit

app.py

@@ -0,0 +1,43 @@
+from transformers import AutoTokenizer, AutoModelWithLMHead
+import gradio as gr
+
+# inference function
+def inference(inp):
+    tokenizer = AutoTokenizer.from_pretrained("GPT-python")
+    model = AutoModelWithLMHead.from_pretrained("GPT-python")
+
+    input_ids = tokenizer.encode(inp, return_tensors="pt")
+    beam_output = model.generate(input_ids, 
+                               max_length=512,
+                               num_beams=10,
+                               temperature=0.7,
+                               no_repeat_ngram_size=5,
+                               num_return_sequences=1,
+                               )
+  
+    output = []
+    for beam in beam_output:
+        out = tokenizer.decode(beam)
+        fout = out.replace("<N>", "\n")
+        output.append(fout)
+
+    return '\n'.join(output)
+
+desc = """
+        Enter some Python code and click submit to see the model's autocompletion.\n
+        
+        Best results have been observed with the prompt of \"import\".\n
+
+        Please note that outputs are reflective of a model trained on a measly 40 MBs of text data for 
+        a single epoch of ~16 GPU hours. Given more data and training time, the autocompletion should be much stronger.\n
+        
+        Computation will take some time.
+        """
+
+# Creates and launches gradio interface
+gr.Interface(fn=inference,
+            inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+            outputs=gr.outputs.Textbox(),
+            title="Generative Python Transformer",
+            description=desc,
+            ).launch()