Create app.py

app.py

@@ -0,0 +1,43 @@
+from predict import *
+from transformers import BloomTokenizerFast, BloomForCausalLM
+
+import os
+import gradio as gr
+
+model_path = "svjack/bloom-dialogue"
+tokenizer = BloomTokenizerFast.from_pretrained(model_path)
+model = BloomForCausalLM.from_pretrained(model_path)
+
+obj = Obj(model, tokenizer)
+
+example_sample = [
+    ["今天天气不错。", 128],
+    ["你饿吗？", 128],
+]
+
+def demo_func(prefix, max_length):
+    max_length = max(int(max_length), 32)
+    l = obj.predict(prefix, max_length=max_length)[0].split("\n-----\n")
+    l_ = []
+    for ele in l:
+        if ele not in l_:
+            l_.append(ele)
+    l = l_
+    assert type(l) == type([])
+    return {
+        "Dialogue Context": l
+    }
+
+demo = gr.Interface(
+        fn=demo_func,
+        inputs=[gr.Text(label = "Prefix"),
+                gr.Number(label = "Max Length", value = 128)
+        ],
+        outputs="json",
+        title=f"Bloom Chinese Dialogue Generator 🐰🌸 demonstration",
+        examples=example_sample if example_sample else None,
+        description = 'This _example_ was **drive** from <br/><b><h4>[https://github.com/svjack/Daliy-Dialogue](https://github.com/svjack/Daliy-Dialogue)</h4></b>\n',
+        cache_examples = False
+    )
+
+demo.launch(server_name=None, server_port=None)