Update app.py

app.py

@@ -1,3 +1,79 @@
-import gradio as gr
+output_dir_merge = "Elliot4AI/Dugong-Llama2-7b-chinese"
+# load base LLM model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    output_dir_merge,
+    low_cpu_mem_usage=True,
+    torch_dtype=torch.float16,
+    load_in_8bit=True,
+)
 
-gr.Interface.load("models/Elliot4AI/Dugong-Llama2-7b-chinese").launch()
+tokenizer = AutoTokenizer.from_pretrained(output_dir_merge)
+
+def run_generation(user_text, top_p, temperature, top_k, max_new_tokens):
+    # Get the model and tokenizer, and tokenize the user text.
+    model_inputs = tokenizer([user_text], return_tensors="pt").input_ids.cuda()
+
+    # Start generation on a separate thread, so that we don't block the UI. The text is pulled from the streamer
+    # in the main thread. Adds timeout to the streamer to handle exceptions in the generation thread.
+    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        inputs=model_inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        temperature=float(temperature),
+        top_k=top_k
+        # repetition_penalty=2.0
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    # Pull the generated text from the streamer, and update the model output.
+    model_output = ""
+    for new_text in streamer:
+        model_output += new_text
+        yield model_output
+    return model_output
+
+
+def reset_textbox():
+    return gr.update(value='')
+with gr.Blocks() as demo:
+    with gr.Tab("PatentQA-Dugong-Llama2-7b-chinese Agent"):
+        gr.Markdown(
+        "# 🤗 PatentQA_Dugong 🔥PatentQA_Dugong Agent🔥 \n"
+        "Dugong是一个用中文微调的Llama2-7b的模型， 微调后中文回答更顺畅 "
+        "目前采用流式输出"
+        "🤗💛"
+    )
+        # gr.Markdown("PatentQA_Dugong Agent: Dugong是一个用中文微调的Llama2-7b的模型， 微调后中文回答更顺畅，并且具有丰富英业达专利知识的人工智能助手，可以回答专利的相关信息,目前恢复速度稍慢")
+        with gr.Row():
+           with gr.Column(scale=4):
+            user_text = gr.Textbox(
+                placeholder="请输入你的问题",
+                label="问题"
+            )
+            model_output = gr.Textbox(label="回答", lines=10, interactive=False)
+            button_submit = gr.Button(value="提交")
+            clear = gr.ClearButton([user_text, model_output])
+
+           with gr.Column(scale=1):
+            max_new_tokens = gr.Slider(
+                minimum=1, maximum=1000, value=250, step=1, interactive=True, label="最大输出token数量",
+            )
+            top_p = gr.Slider(
+                minimum=0.05, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p (nucleus sampling)",
+            )
+            top_k = gr.Slider(
+                minimum=1, maximum=50, value=50, step=1, interactive=True, label="Top-k",
+            )
+            temperature = gr.Slider(
+                minimum=0.1, maximum=5.0, value=0.8, step=0.1, interactive=True, label="温度",
+            )
+    
+    user_text.submit(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output)
+    button_submit.click(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output)
+    
+demo.queue(max_size=32)
+demo.launch(enable_queue=True)