Update app.py

app.py

@@ -1,33 +1,40 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-tokenizer = AutoTokenizer.from_pretrained("jslin09/bloom-560m-finetuned-fraud")
-model = AutoModelForCausalLM.from_pretrained("jslin09/bloom-560m-finetuned-fraud")
-
-def predict(input, history=[]):
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
-
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-
-    # generate a response 
-    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
-
-    # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
-    return response, history
-
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot()
-    state = gr.State([])
-
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
-
-    txt.submit(predict, [txt, state], [chatbot, state])
-            
-if __name__ == "__main__":
-    demo.launch()
+from transformers import pipeline
+
+#pipe = pipeline("text-generation")
+#gr.Interface.from_pipeline(pipe).launch()
+
+API_URL = "https://api-inference.huggingface.co/models/jslin09/bloom-560m-finetuned-fraud"
+headers = {"Authorization": "Bearer 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"} # Read only
+
+description = "Legal Document Drafting with BLOOM"
+api_key="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"
+examples=[
+          ["闕很大明知金融帳戶之存摺、提款卡及密碼係供自己使用之重要理財工具，"],
+          ["森上梅前明知其無資力支付酒店消費，亦無付款意願，竟意圖為自己不法之所有，"]
+]
+
+iface = gr.Interface.load(
+    "huggingface/jslin09/bloom-560m-finetuned-fraud",
+    title="Drafting",
+    inputs=[
+        gr.Textbox(lines=10, label="Prompt", value="闕很大明知金融帳戶之存摺、提款卡及密碼係供自己使用之重要理財工具，"),  # prompt
+        gr.Slider(10, 200, step=10, value=100),  # token_count
+        gr.Slider(0.2, 2.0, step=0.1, value=1.0),  # temperature
+        gr.Slider(0.0, 1.0, step=0.05, value=0.8),  # top_p
+        gr.Slider(0.0, 1.0, step=0.1, value=0.1),  # presencePenalty
+        gr.Slider(0.0, 1.0, step=0.1, value=0.1),  # countPenalty
+    ],
+    outputs=gr.Textbox(label="生成的草稿", lines=28),
+    description=description,
+    examples=examples,
+    api_key=api_key,
+).queue()
+
+demo = gr.TabbedInterface(
+    [iface], ["分頁標籤"],
+    title="Legal Document Drafting",
+)
+
+demo.queue()
+demo.launch(share=False)