a

app.py

@@ -95,16 +95,15 @@ with gr.Blocks(title="Automatic Prompt Engineer", theme=gr.themes.Glass()) as de
     with gr.Row():
         with gr.Column():
             openai_key2 = gr.Textbox(type='password', label='输入 API key')
-            n_human = gr.Slider(label="人工指令数", minimum=1, maximum=5, step=1, value=1)
+            n_human = gr.Slider(label="人工指令数", minimum=1, maximum=5, step=1, value=2)
             n_machine = gr.Slider(label="机器指令数", minimum=1, maximum=5, step=1, value=1)
-            n_instruct = gr.Slider(label="生成指令数", minimum=1, maximum=100, step=5, value=1, help="生成指令数>人工+机器")
+            n_instruct = gr.Slider(label="生成指令数", minimum=1, maximum=100, step=1, value=4, help="生成指令数>人工+机器")
             seed_file = gr.File(label='上传json文件, 格式参考./self/data/seed_task.json')
 
         with gr.Column():
             self_prompt_input = gr.Textbox(max_lines=100, lines=20, interative=True,
                                            placeholder=self_prompt,
                                            value='', label="Prompt for self-instruct")
-    init_self = gr.Button("初始化")
     self_instance = gr.State()
 
     gr.Markdown('\n\n')
@@ -152,7 +151,7 @@ with gr.Blocks(title="Automatic Prompt Engineer", theme=gr.themes.Glass()) as de
     SELF Callback
     """
     # 1. 加载种子文件
-    init_self.click(init_instance, inputs=[seed_file, openai_key2, n_human, n_machine, n_instruct, self_prompt_input],
+    seed_file.submit(init_instance, inputs=[seed_file, openai_key2, n_human, n_machine, n_instruct, self_prompt_input],
                     outputs=[self_instance])
 
     # 2. 生成

requirements.txt

@@ -4,4 +4,5 @@ openai
 langchain
 pandas
 rouge_score
-transformers
+transformers
+pytorch

self/generate.py

@@ -89,10 +89,8 @@ class SELF(object):
         # build few-shot
         few_shot = gen_few_shot_prompt(self.sample_few_shot)
         few_shot += SELF.prefix.format(id=self.first_id)  # 新生成的指令
-        print(few_shot)
         # generate
         result = self.chain({'few_shot': few_shot, 'n_instruct': self.n_instruct})
-        print(result)
         return result
 
     def decode_response(self, response):
@@ -142,6 +140,8 @@ class SELF(object):
             with Pool(os.cpu_count()) as p:
                 rouge_scores = p.map(partial(rouge_scorer._score_lcs, self.all_instruction_tokens), inst_tokens)
             rouge_l = [score.fmeasure for score in rouge_scores]
+            print(rouge_scores)
+            print(rouge_l)
             top10_sim_inst = {
                 self.all_instruction[i]: rouge_l[i] for i in np.argsort(rouge_l)[-10:][::-1]
             }