Update app.py

app.py

@@ -1,66 +1,66 @@
 import gradio as gr
-from langchain.llms import OpenAI
-from langchain.llms.fake import FakeListLLM
-
-from langchain import PromptTemplate, FewShotPromptTemplate
-from langchain.utilities import TextRequestsWrapper
-import json
-
-def generate_prompt(embedding_url, extra_prompt, count):
-
-    output = TextRequestsWrapper().get(embedding_url)
-    result = json.loads(output)
-
-    example_formatter_template = "标题: {sentence}\n热度: {hot_level}"
-    example_prompt = PromptTemplate(
-        input_variables=["sentence", "hot_level"],
-        template=example_formatter_template,
-    )
-
-    few_shot_prompt = FewShotPromptTemplate(
-        examples=result["data"],
-        example_prompt=example_prompt,
-        prefix="你是一个短视频方专家，你的职责是为短视频的制作进行选题。\n以下是某视频站点的最新热词\n\n",
-        # The suffix is some text that goes after the examples in the prompt.
-        # Usually, this is where the user input will go
-        suffix="{extra_prompt}\n\n请根据以上内容草拟{count}个吸引人的短视频的标题:",
-        # The input variables are the variables that the overall prompt expects.
-        input_variables=["extra_prompt", "count"],
-        # The example_separator is the string we will use to join the prefix, examples, and suffix together with.
-        example_separator="\n\n",
-    )
-
-    # We can now generate a prompt using the `format` method.
-    # print(few_shot_prompt.format(count="5"))
-    return few_shot_prompt.format(extra_prompt=extra_prompt, count=count)
-
-def generate_topics(
-        openai_api_key: str, 
-        embedding_url: str,
-        extra_prompt: str, 
-        count: int, 
-        repeat: int
-    ):
-    if openai_api_key.strip() == "":
-        return "请输入OPENAI API KEY"
+# from langchain.llms import OpenAI
+# from langchain.llms.fake import FakeListLLM
+
+# from langchain import PromptTemplate, FewShotPromptTemplate
+# from langchain.utilities import TextRequestsWrapper
+# import json
+
+# def generate_prompt(embedding_url, extra_prompt, count):
+
+#     output = TextRequestsWrapper().get(embedding_url)
+#     result = json.loads(output)
+
+#     example_formatter_template = "标题: {sentence}\n热度: {hot_level}"
+#     example_prompt = PromptTemplate(
+#         input_variables=["sentence", "hot_level"],
+#         template=example_formatter_template,
+#     )
+
+#     few_shot_prompt = FewShotPromptTemplate(
+#         examples=result["data"],
+#         example_prompt=example_prompt,
+#         prefix="你是一个短视频方专家，你的职责是为短视频的制作进行选题。\n以下是某视频站点的最新热词\n\n",
+#         # The suffix is some text that goes after the examples in the prompt.
+#         # Usually, this is where the user input will go
+#         suffix="{extra_prompt}\n\n请根据以上内容草拟{count}个吸引人的短视频的标题:",
+#         # The input variables are the variables that the overall prompt expects.
+#         input_variables=["extra_prompt", "count"],
+#         # The example_separator is the string we will use to join the prefix, examples, and suffix together with.
+#         example_separator="\n\n",
+#     )
+
+#     # We can now generate a prompt using the `format` method.
+#     # print(few_shot_prompt.format(count="5"))
+#     return few_shot_prompt.format(extra_prompt=extra_prompt, count=count)
+
+# def generate_topics(
+#         openai_api_key: str, 
+#         embedding_url: str,
+#         extra_prompt: str, 
+#         count: int, 
+#         repeat: int
+#     ):
+#     if openai_api_key.strip() == "":
+#         return "请输入OPENAI API KEY"
     
-    llm = OpenAI(temperature=.7, openai_api_key=openai_api_key)
+#     llm = OpenAI(temperature=.7, openai_api_key=openai_api_key)
 
-    prompt = generate_prompt(embedding_url, extra_prompt, count)
-    print(prompt)
+#     prompt = generate_prompt(embedding_url, extra_prompt, count)
+#     print(prompt)
     
-    llm_result = llm.generate([prompt]*repeat)
+#     llm_result = llm.generate([prompt]*repeat)
 
-    answer = "" if repeat == 1 else f"以下是合并的{str(repeat)}次选题结果：\n\n"
-    for i in range(len(llm_result.generations)):
-        answer += f"{'下一个：' if i > 0 else ''}" + llm_result.generations[i][0].text + "\n\n"
+#     answer = "" if repeat == 1 else f"以下是合并的{str(repeat)}次选题结果：\n\n"
+#     for i in range(len(llm_result.generations)):
+#         answer += f"{'下一个：' if i > 0 else ''}" + llm_result.generations[i][0].text + "\n\n"
     
-    print(answer)
+#     print(answer)
 
-    token_usage = llm_result.llm_output["token_usage"]["total_tokens"] if llm_result.llm_output else 0
-    answer += f"##总共消耗Token数：{str(token_usage)}"
+#     token_usage = llm_result.llm_output["token_usage"]["total_tokens"] if llm_result.llm_output else 0
+#     answer += f"##总共消耗Token数：{str(token_usage)}"
 
-    return answer
+#     return answer
 
 with gr.Blocks() as demo:
   gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
@@ -107,8 +107,7 @@ with gr.Blocks() as demo:
   # btn2 = gr.Button("生成视频创作脚本")
   # output2 = gr.Textbox(label="Scripts")
 
-  btn.click(generate_topics, [openai_api_key, embedding_url, extra_prompt, count, repeat], [topics_output])
+  #btn.click(generate_topics, [openai_api_key, embedding_url, extra_prompt, count, repeat], [topics_output])
   # btn2.click(chat.generateScripts, [], [chatbot])
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()