update the dependency and import error

app.py

@@ -15,7 +15,7 @@ from sqlitedict import SqliteDict
 
 import gradio as gr
 
-from langchain import PromptTemplate, LLMChain
+from langchain import PromptTemplate, LLMChain, ConversationChain
 from langchain.agents import Tool
 from langchain.agents import load_tools
 from langchain.agents import initialize_agent
@@ -53,18 +53,18 @@ from langchain.document_loaders import YoutubeLoader
 from azure_utils import AzureVoiceData
 from polly_utils import PollyVoiceData, NEURAL_ENGINE
 from contextlib import closing
-from langchain_experimental.agents import create_pandas_dataframe_agent
+from langchain.agents import create_pandas_dataframe_agent
 import pandas as pd
 
 #os env
 os.environ["OPENAI_API_TYPE"] = "azure"
-os.environ["OPENAI_API_VERSION"] = "1106-Preview"
-os.environ["OPENAI_API_BASE"] = "https://chairman-azureopenai-australiaeast.openai.azure.com/"
-os.environ["OPENAI_API_KEY"] = "ac523b401b664f9e800d43933f0c5df5"
+os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
+os.environ["OPENAI_API_BASE"] = "https://hh-azure-openai-poc.openai.azure.com/"
+os.environ["OPENAI_API_KEY"] = "41dd3ccda6a2489db375f3fe2a440953"
 os.environ["SERPAPI_API_KEY"] = "a5b67b8805b4e12b0ae147c9c6b2a7dbf3ab84fca5f24e531b6963b1f7fc1ff7"
 
-global_deployment_id = "gpt-4-1106-preview"
-global_model_name = "gpt-4"
+global_deployment_id = "gpt-35-turbo-16k"
+global_model_name = "gpt-35-turbo-16k"
 
 #chroma settings
 chroma_api_impl = "HH_Azure_Openai"
@@ -123,11 +123,11 @@ def ai_answer(answer):
 
 def get_openaiembeddings():
     return OpenAIEmbeddings(
-        deployment="text-embedding-ada-002",
+        deployment="CivetGPT_embedding",
         model="text-embedding-ada-002",
-        openai_api_base="https://hh-azure-openai-poc.openai.azure.com/",
+        openai_api_base="https://civet-project-001.openai.azure.com/",
         openai_api_type="azure",
-        openai_api_key = "b3cfb72345be4001a470e827a694d083",
+        openai_api_key = "0e3e5b666818488fa1b5cb4e4238ffa7",
         chunk_size=1
     )
 
@@ -302,7 +302,9 @@ def chat_conversation():
         ai_response = chat(history.messages)
         ai_answer(ai_response.content)
 
-def local_vector_search(question_str,chat_history, collection_name = hr_collection_name):
+def local_vector_search(question_str,
+                        chat_history,
+                        collection_name = hr_collection_name):
     embedding = get_openaiembeddings()
     vectorstore = Chroma( embedding_function=embedding,
                           collection_name=collection_name,
@@ -321,7 +323,6 @@ def local_vector_search(question_str,chat_history, collection_name = hr_collecti
         model_name= global_model_name,
         temperature = 0.0)
 
-
     prompt = PromptTemplate(
         template=get_prompt_template_string(),
         input_variables=["question","chat_history"]
@@ -600,6 +601,19 @@ def kh_update_km(files):
 
     return num_of_collection(tmp_collection)
 
+def generate_autolayout(description, template):
+    llm = AzureChatOpenAI(
+        deployment_name=global_deployment_id,
+        model_name=global_model_name,
+        temperature=0.0)
+    _template= PromptTemplate(
+        input_variables=["text"], template=template
+    )
+    llm_chain = LLMChain(llm=llm, prompt=_template)
+    layout_output = llm_chain.run(description)
+    print(layout_output)
+    return layout_output
+
 class Logger:
     def __init__(self, filename):
         self.terminal = sys.stdout
@@ -646,7 +660,6 @@ def lunch_style(demo, logs=gr.Text()):
 def gradio_run():
     print("User Login")
     with gr.Blocks(theme='bethecloud/storj_theme') as demo:
-
         with gr.Row():
             gr.Markdown("# HH Azure Openai Demo")
         #Header section
@@ -771,6 +784,7 @@ def gradio_run():
                                                                                                  upload_button,
                                                                                                  summary_text)
         #3rd youtube
+        """
         with gr.Tab("Youtube 影片摘要"):
             with gr.Row():
                 with gr.Column(scale=1):
@@ -781,7 +795,9 @@ def gradio_run():
                 with gr.Column(scale=1):
                     youtube_summary_textbox=gr.Textbox(interactive=False, label="AI 解析", lines=20)
             youtube_analysis_btn.click(youtube_summary,youtube_link,youtube_summary_textbox)
+        """
         #4th 相信人員統計助手
+
         with gr.Tab("相信人員統計助手"):
 
             mypath = root_file_path + believe_source_path
@@ -859,7 +875,6 @@ def gradio_run():
                     '全台灣的員工, 每人每天問五個問題, 1個月花費多少錢?',
                     '如果龍華廠區的員工每人每天問3個問題,台灣員工每人每天問7個問題, 請問這樣一個月多少錢?'
                     ], label="訊息範例", inputs=tmp_msg)
-
         with gr.Tab("法務AI幫手"):
             legal_path = "./data/"
             quotation_file = "legal_quotation_prompt.txt"
@@ -924,11 +939,9 @@ def gradio_run():
                                                 label="2. Prompt",
                                                 interactive=True)
                     prompt_textbox.change(change_prompt, inputs=prompt_textbox)
-                    with gr.Row():
-                        with gr.Column():
-                            saveBtn = gr.Button("保存現有Prompt")
-                        with gr.Column():
-                            restoreBtn = gr.Button("回覆預設Prompt")
+
+                    saveBtn = gr.Button("保存現有Prompt")
+                    restoreBtn = gr.Button("回覆預設Prompt")
 
                     gr.Markdown("""
                     ---
@@ -953,6 +966,39 @@ def gradio_run():
             upload_button.upload(upload_large_file, upload_button, file_name_field).\
                 then(change_prompt,inputs=prompt_textbox).\
                 then(summary_large_file, upload_button, summary_text)
+        with gr.Tab("AI layout"):
+            with gr.Row():
+                def change_prompt(inputString):
+                    template_string = inputString
+                    return template_string
+                def auto_layout(description, template):
+                    html_string = generate_autolayout(description, template)
+                    return html_string
+                with gr.Column(scale=1):
+                    file_list = gr.Textbox(get_hr_files, label="1. 已存在知識庫的檔案",
+                                           placeholder="沒有任何檔案存在", max_lines=5, lines=5)
+                    upload_button = gr.UploadButton("上傳UX/UI 知識庫檔案(text,pdf,docx,csv)",file_types=["text", ".pdf", ".docx", ".csv"],file_count="multiple")
+                    upload_button.upload(update_hr_km, inputs=upload_button, outputs=file_list)
+                    cleanDataBtn = gr.Button(value="刪除所有知識以及檔案")
+                    cleanDataBtn.click(clear_hr_datas, outputs=file_list)
+                with gr.Column(scale=4):
+                    autolayout_template = """你是一個資深的UX designer, 請依下方的需求, 
+給我一個web UI 的設計畫面, 使用html 語法; 除html 語法之外, 你不要說其他的話
+                    
+需求:
+'''
+{text}
+'''                  
+                    """
+                    prompt_textbox = gr.Textbox(autolayout_template, lines=8, max_lines=8, label="2. Prompt")
+                    update_btn = gr.Button("更新Prompt")
+            with gr.Row():
+                msg = gr.Textbox(placeholder="輸入說明讓AI 明白你想要的畫面", label="3. 描述")
+            with gr.Row():
+                content = gr.HTML("")
+            update_btn.click(change_prompt, prompt_textbox, prompt_textbox)
+            msg.submit(auto_layout, [msg,prompt_textbox], content, queue=True)
+
         demo.queue(concurrency_count=10)
         lunch_style(demo,console)
 
@@ -963,4 +1009,3 @@ def test():
 
 gradio_run()
 
-

data/legal_quotation_prompt.txt

@@ -2,7 +2,7 @@
 你是一位超級助理, 十分擅長從大量文字中擷取摘要.
 以下用 ''' 包含的是報價單的內容，幫我生成一份2,000個中文字以內報價單摘要，摘要需要包含以下項目：
 
-1. 標的名稱: 報價單中所列出的產品或服務的名稱。
+1. 標的名稱: 報價單中所列出的產品或服務的名稱, 若是無法清楚識別正確的產品名稱或服務名稱, 請寫 '產品/名稱 無法辨識'
 2. 價格: 報價單中所列出的每個產品或服務的價格, 一定要有正確的幣別與金額數字.
 3. 付款內容: 報價單中所列出的付款方式和相關內容, 包括訂金, 交貨款和保留款的金額和支付方式; 除了各款項的交付百分比, 也需要有正確的金額與幣別.
 4. 交貨時間: 報價單中所列出的產品或服務的交付的日期或時間範圍。

requirements.txt

@@ -1,7 +1,6 @@
 boto3
 sqlitedict
 langchain
-langchain_experimental
 pandas
 chroma
 openai
@@ -13,5 +12,4 @@ pydantic==1.10.8
 hnswlib
 gradio_client==0.2.7
 tiktoken
-unstructured[all-docs]
-numexpr
+unstructured[all-docs]