Create app.py

app.py

@@ -0,0 +1,47 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+  
+def get_pipe(name):
+    tokenizer = AutoTokenizer.from_pretrained(name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(name)
+    pipe = pipeline(
+	"summarization", model=model, tokenizer=tokenizer, framework="pt"
+    )
+    return pipe
+model_names = ['bigscience/T0_3B'] #, 'bigscience/T0p', 'bigscience/T0pp']
+#model_names = ['bigscience/T0_3B','bigscience/T0'] #, 'bigscience/T0p', 'bigscience/T0pp']
+pipes = [get_pipe(name) for name in model_names]
+def _fn(text, do_sample, min_length, max_length, temperature, top_p, pipe):
+    out = pipe(
+        text,
+        do_sample=do_sample,
+        min_length=min_length,
+        max_length=max_length,
+        temperature=temperature,
+        top_p=top_p,
+        truncation=True,
+    )
+    return out[0]["summary_text"]
+def fn(*args):
+    return [_fn(*args, pipe=pipe) for pipe in pipes]
+import gradio as gr
+interface = gr.Interface(
+    fn,
+    inputs=[
+        gr.inputs.Textbox(lines=10, label="input text"),
+        gr.inputs.Checkbox(label="do_sample", default=True),
+        gr.inputs.Slider(1, 128, step=1, default=64, label="min_length"),
+        gr.inputs.Slider(1, 128, step=1, default=64, label="max_length"),
+        gr.inputs.Slider(0.0, 1.0, step=0.1, default=1, label="temperature"),
+        gr.inputs.Slider(0.0, 1.0, step=0.1, default=1, label="top_p"),
+    ],
+    outputs=[
+        gr.outputs.Textbox(label=f"output by {name}") for name in model_names
+    ],
+    #examples=[[ex] for ex in examples],
+    title="T0 playground",
+    description="""
+    This is a playground for playing around with T0 models.
+    See https://huggingface.co/bigscience/T0 for more details
+""",
+)
+interface.launch()