first commit

app.py

@@ -0,0 +1,114 @@
+import gradio as gr
+
+
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer
+)
+from peft import PeftModel
+import torch
+
+model_path = "Qwen/Qwen1.5-1.8B-Chat"
+lora_path = "AngoHF/EssayGPT" #+ "/checkpoint-100"
+
+if torch.cuda.is_available():
+    device = "cuda:0"
+else:
+    device = "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_path,
+)
+config_kwargs = {"device_map": device}
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.float16,
+    **config_kwargs
+)
+
+model = PeftModel.from_pretrained(model, lora_path)
+model = model.merge_and_unload()
+model.eval()
+
+# model.config.use_cache = True
+# model.to("cpu")
+# model.save_pretrained("/data/ango/EssayGPT")
+
+# tokenizer.save_pretrained("/data/ango/EssayGPT")
+
+
+MAX_MATERIALS = 4
+
+
+def call(related_materials, materials, question):
+    query_texts = [f"材料{i + 1}\n{material}" for i, material in enumerate(materials) if i in related_materials]
+    query_texts.append(f"问题:{question}")
+    query = "\n".join(query_texts)
+    messages = [
+        {"role": "system", "content": "请你根据以下提供的材料来回答问题"},
+        {"role": "user", "content": query}
+    ]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to(device)
+    print(len(model_inputs.input_ids[0]))
+    generated_ids = model.generate(
+        model_inputs.input_ids,
+        max_length=8096
+    )
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return response
+
+
+def create_ui():
+    with gr.Blocks() as app:
+        gr.Markdown("""<center><font size=8>EssayGPT-申论大模型</center>""")
+        gr.Markdown(
+            """<center><font size=4>1.把材料填入对应位置 2.输入问题和要求 3.选择解答问题需要的相关材料 4.点击"提问!"</center>""")
+        with gr.Row():
+            with gr.Column():
+                materials = []
+
+                for i in range(MAX_MATERIALS):
+                    with gr.Tab(f"材料{i + 1}"):
+                        materials.append(gr.Textbox(label="材料内容"))
+            with gr.Column():
+                related_materials = gr.Dropdown(
+                    choices=list(range(1, MAX_MATERIALS + 1)), multiselect=True,
+                    label="问题所需相关材料")
+                question = gr.Textbox(label="问题")
+                submit = gr.Button("提问！")
+                answer = gr.Textbox(label="回答")
+        build_ui({"materials": materials, "related_materials": related_materials, "question": question,
+                  "submit": submit, "answer": answer})
+    return app
+
+
+def build_ui(components):
+    def func(related_materials, question, *materials):
+        if not related_materials:
+            return "请选择问题所需相关材料"
+        related_materials = [i - 1 for i in related_materials]
+        return call(related_materials, materials, question)
+
+    components["submit"].click(func,
+                               [components["related_materials"], components["question"], *components["materials"]],
+                               components["answer"])
+
+
+def run():
+    app = create_ui()
+    app.queue()
+    app.launch(share=True)
+
+
+if __name__ == '__main__':
+    run()