Update app.py

app.py

@@ -26,7 +26,7 @@ PINECONE_ENV = os.environ.get("PINECONE_ENV", "asia-northeast1-gcp")
 PINECONE_INDEX = os.environ.get("PINECONE_INDEX", "3gpp")
 
 PINECONE_LINK  = "[Pinecone](https://www.pinecone.io)"
-LANGCHAIN_LINK  = "[LangChain](https://python.langchain.com/en/latest/index.html)"
+LANGCHAIN_LINK = "[LangChain](https://python.langchain.com/en/latest/index.html)"
 
 EMBEDDING_MODEL = os.environ.get("PINECONE_INDEX", "sentence-transformers/all-mpnet-base-v2")
 
@@ -35,10 +35,13 @@ TOP_K_DEFAULT = 10
 TOP_K_MAX = 25
 
 
-BUTTON_MIN_WIDTH = 210
+BUTTON_MIN_WIDTH = 215
 
-STATUS_NOK = "404-MODEL UNREADY-critical"
-STATUS_OK  = "200-MODEL LOADED-9cf"
+LLM_NULL = "LLM-UNLOAD-critical"
+LLM_DONE = "LLM-LOADED-9cf"
+
+DB_NULL = "DB-UNLOAD-critical"
+DB_DONE = "DB-LOADED-9cf"
 
 FORK_BADGE = "Fork-HuggingFace Space-9cf"
 
@@ -46,10 +49,10 @@ FORK_BADGE = "Fork-HuggingFace Space-9cf"
 def get_logo(inputs, logo) -> str:
     return f"""https://img.shields.io/badge/{inputs}?style=flat&logo={logo}&logoColor=white"""
 
-def get_status(inputs) -> str:
+def get_status(inputs, logo, pos) -> str:
     return f"""<img
-    src   = "{get_logo(inputs, "openai")}";
-    style = "margin: 0 auto;"
+    src   = "{get_logo(inputs, logo)}";
+    style = "margin: 0 auto; float:{pos}"
     >"""
     
 
@@ -57,13 +60,17 @@ KEY_INIT   = "Initialize Model"
 KEY_SUBMIT = "Submit"
 KEY_CLEAR  = "Clear"
 
-MODEL_NULL = get_status(STATUS_NOK)
-MODEL_DONE = get_status(STATUS_OK)
-
+MODEL_NULL = get_status(LLM_NULL, "openai", "left")
+MODEL_DONE = get_status(LLM_DONE, "openai", "left")
 
-MODEL_WARNING = f"Please paste your **{OPENAI_API_LINK}** and then **{KEY_INIT}**"
+DOCS_NULL = get_status(DB_NULL, "processingfoundation", "right")
+DOCS_DONE = get_status(DB_DONE, "processingfoundation", "right")
 
 TAB_1 = "Chatbot"
+TAB_2 = "Details"
+TAB_3 = "Database"
+
+
 
 FAVICON = './icon.svg'
 
@@ -73,8 +80,17 @@ LLM_LIST = ["gpt-3.5-turbo", "text-davinci-003"]
 DOC_1 = '3GPP'
 DOC_2 = 'HTTP2'
 
-DOC_SUPPORTED = [DOC_1, DOC_2]
-DOC_DEFAULT = [DOC_1]
+DOC_SUPPORTED = [DOC_1]
+DOC_DEFAULT   = [DOC_1]
+DOC_LABEL = "Reference Docs"
+
+
+MODEL_WARNING = f"Please paste your **{OPENAI_API_LINK}** and then **{KEY_INIT}**"
+
+DOCS_WARNING = f"""Database Unloaded
+Please check your **{TAB_3}** config and then **{KEY_INIT}**
+Or you could uncheck **{DOC_LABEL}** to ask LLM directly"""
+
 
 webui_title = """
 # OpenAI Chatbot Based on Vector Database
@@ -96,51 +112,37 @@ init_message = f"""This demonstration website is based on \
 
 def init_model(api_key, emb_name, db_api_key, db_env, db_index):
     try:
-        if (api_key and api_key.startswith("sk-") and len(api_key) > 50) and \
-        (emb_name and db_api_key and db_env and db_index):
-            
-            embeddings = HuggingFaceEmbeddings(model_name=emb_name)
-
-            pinecone.init(api_key     = db_api_key,
-                          environment = db_env)
+        if not (api_key and api_key.startswith("sk-") and len(api_key) > 50):
+            return None,MODEL_NULL+DOCS_NULL,None,None,None,None
+        
 
-            #llm = OpenAI(temperature=OPENAI_TEMP, model_name="gpt-3.5-turbo-0301")
 
-            
-            llm_dict = {}
-            for llm_name in LLM_LIST:
-                if llm_name == "gpt-3.5-turbo":
-                    llm_dict[llm_name] = ChatOpenAI(model_name=llm_name,
-                                                    temperature = OPENAI_TEMP,
-                                                    openai_api_key = api_key)
-                else:
-                    llm_dict[llm_name] = OpenAI(model_name=llm_name,
+        llm_dict = {}
+        for llm_name in LLM_LIST:
+            if llm_name == "gpt-3.5-turbo":
+                llm_dict[llm_name] = ChatOpenAI(model_name=llm_name,
                                                 temperature = OPENAI_TEMP,
                                                 openai_api_key = api_key)
-
-            '''
-            ChatOpenAI(model_name="gpt-3.5-turbo",
-                             temperature = OPENAI_TEMP,
-                             openai_api_key = api_key)
-            chain_1 = load_qa_chain(llm, chain_type="stuff")
+            else:
+                llm_dict[llm_name] = OpenAI(model_name=llm_name,
+                                            temperature = OPENAI_TEMP,
+                                            openai_api_key = api_key)
+                    
+        if not (emb_name and db_api_key and db_env and db_index):
+            return api_key,MODEL_DONE+DOCS_NULL,llm_dict,None,None,None
             
-            #LLMChain(llm=llm, prompt=condense_question_prompt)
+        embeddings = HuggingFaceEmbeddings(model_name=emb_name)
 
-            chain_2 = LLMChain(llm = llm,
-                             prompt = PromptTemplate(template='{question}',
-                                                     input_variables=['question']),
-                             output_key = 'output_text')
-                             '''
-            
-            db = Pinecone.from_existing_index(index_name = db_index,
-                                              embedding  = embeddings)
+        pinecone.init(api_key     = db_api_key,
+                      environment = db_env)
+        db = Pinecone.from_existing_index(index_name = db_index,
+                                          embedding  = embeddings)
 
-            return api_key, MODEL_DONE, llm_dict, None, db, None
-        else:
-            return None,MODEL_NULL,None,None,None,None
+        return api_key, MODEL_DONE+DOCS_DONE, llm_dict, None, db, None
+        
     except Exception as e:
         print(e)
-        return None,MODEL_NULL,None,None,None,None
+        return None,MODEL_NULL+DOCS_NULL,None,None,None,None
 
 
 def get_chat_history(inputs) -> str:
@@ -176,7 +178,7 @@ def bot(box_message, ref_message,
     question = box_message[-1][0]
     history  = box_message[:-1]
     
-    if (not llm_dict) or (not doc_check) or (not db):
+    if (not llm_dict):
         box_message[-1][1] = MODEL_WARNING
         return box_message, "", ""
 
@@ -190,6 +192,10 @@ def bot(box_message, ref_message,
     llm = llm_dict[llm_dropdown]
     
     if DOC_1 in doc_list:
+        if (not db):
+            box_message[-1][1] = DOCS_WARNING
+            return box_message, "", ""
+        
         chain = load_qa_chain(llm, chain_type="stuff")
         docs = doc_similarity(ref_message, db, top_k)
         delta_top_k = top_k - len(docs)
@@ -251,7 +257,7 @@ with gr.Blocks(
         with gr.Column(scale=1, min_width=BUTTON_MIN_WIDTH):
             
             init = gr.Button(KEY_INIT) #.style(full_width=False)
-            model_statusbox = gr.HTML(MODEL_NULL)
+            model_statusbox = gr.HTML(MODEL_NULL+DOCS_NULL)
     
     with gr.Tab(TAB_1):
         with gr.Row():
@@ -261,7 +267,7 @@ with gr.Blocks(
             with gr.Column(scale=1, min_width=BUTTON_MIN_WIDTH):
                 doc_check = gr.CheckboxGroup(choices = DOC_SUPPORTED,
                                              value   = DOC_DEFAULT,
-                                             label   = "Reference Docs",
+                                             label   = DOC_LABEL,
                                              interactive=True)
                 llm_dropdown = gr.Dropdown(LLM_LIST,
                                            value=LLM_LIST[0],
@@ -281,7 +287,7 @@ with gr.Blocks(
                 submit = gr.Button(KEY_SUBMIT,variant="primary")
                 
 
-    with gr.Tab("Details"):
+    with gr.Tab(TAB_2):
         top_k = gr.Slider(1,
                           TOP_K_MAX,
                           value=TOP_K_DEFAULT,
@@ -290,7 +296,7 @@ with gr.Blocks(
                           interactive=True)
         detail_panel = gr.Chatbot(label="Related Docs")
     
-    with gr.Tab("Database"):
+    with gr.Tab(TAB_3):
         with gr.Row():
             emb_textbox = gr.Textbox(
                 label = "Embedding Model",