debug

app.py

@@ -21,437 +21,442 @@ import streamlit as st
 import subprocess
 
 
-
-if 'logging' not in st.session_state:
-    weaviate_logger = logging.getLogger("httpx")
-    weaviate_logger.setLevel(logging.WARNING)
-    logger = logging.getLogger(__name__)
-    logging.basicConfig(level=logging.INFO)
-    st.session_state.weaviate_logger = weaviate_logger
-    st.session_state.logger = logger
-else:
-    weaviate_logger = st.session_state.weaviate_logger
-    logger = st.session_state.logger
-
-
-def runStartup():
-    logger.info("### Running startup.sh")
-    result = ""
-    try:
-        result = subprocess.run("bash startup.sh &",shell=True,capture_output=True,text=True,timeout=120)
-        logger.info(f"startup.sh stdout:  {result.stdout}")
-        logger.info(f"startup.sh stderr:  {result.stderr}")
-        logger.info(f"Return code: {result.returncode}")
-    except:
-        logger.error(f"subprocess.run() encountered error.")
-    logger.info("### Running startup.sh complete")
-if 'runStartup' not in st.session_state:
-    st.session_state.runStartup = True
-    runStartup()
-
-
-
-######################################################################
-# MAINLINE
-#
-logger.info("#### MAINLINE ENTERED.")
-
-# Function to load the CSS file
-def load_css(file_name):
-    with open(file_name) as f:
-        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
-
-# Load the custom CSS
-if 'load_css' not in st.session_state:
-    load_css(".streamlit/main.css")
-    st.session_state.load_css = True
-
-st.markdown("<h1 style='text-align: center; color: #666666;'>Vector Database RAG Proof of Concept</h1>", unsafe_allow_html=True)
-st.markdown("<h6 style='text-align: center; color: #666666;'>V1</h6>", unsafe_allow_html=True)
-
-#pathString = "/Users/660565/KPSAllInOne/ProgramFilesX86/WebCopy/DownloadedWebSites/LLMPOC_HTML" 
-pathString = "/app/inputDocs"
-chunks = []
-webpageDocNames = []
-page_contentArray = []
-webpageChunks = []
-webpageTitles = []
-webpageChunksDocNames = []
-
-######################################################
-# Connect to the Weaviate vector database.
-#if 'client' not in st.session_state:
-logger.info("#### Create Weaviate db client connection.")
-client = weaviate.WeaviateClient(
-    connection_params=ConnectionParams.from_params(
-        http_host="localhost",
-        http_port="8080",
-        http_secure=False,
-        grpc_host="localhost",
-        grpc_port="50051",
-        grpc_secure=False
-    ),
-    additional_config=AdditionalConfig(
-        timeout=Timeout(init=60, query=1800, insert=1800),  # Values in seconds
+try:
+    if 'logging' not in st.session_state:
+        weaviate_logger = logging.getLogger("httpx")
+        weaviate_logger.setLevel(logging.WARNING)
+        logger = logging.getLogger(__name__)
+        logging.basicConfig(level=logging.INFO)
+        st.session_state.weaviate_logger = weaviate_logger
+        st.session_state.logger = logger
+    else:
+        weaviate_logger = st.session_state.weaviate_logger
+        logger = st.session_state.logger
+
+
+    def runStartup():
+        logger.info("### Running startup.sh")
+        result = ""
+        try:
+            result = subprocess.run("bash startup.sh 2>1 > /app/startup.log &",shell=True,capture_output=True,text=True,timeout=120)
+            logger.info(f"startup.sh stdout:  {result.stdout}")
+            logger.info(f"startup.sh stderr:  {result.stderr}")
+            logger.info(f"Return code: {result.returncode}")
+        except:
+            logger.error(f"subprocess.run() encountered error.")
+        logger.info("### Running startup.sh complete")
+    if 'runStartup' not in st.session_state:
+        st.session_state.runStartup = True
+        runStartup()
+
+
+
+    ######################################################################
+    # MAINLINE
+    #
+    logger.info("#### MAINLINE ENTERED.")
+
+    # Function to load the CSS file
+    def load_css(file_name):
+        with open(file_name) as f:
+            st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+    # Load the custom CSS
+    if 'load_css' not in st.session_state:
+        load_css(".streamlit/main.css")
+        st.session_state.load_css = True
+
+    st.markdown("<h1 style='text-align: center; color: #666666;'>Vector Database RAG Proof of Concept</h1>", unsafe_allow_html=True)
+    st.markdown("<h6 style='text-align: center; color: #666666;'>V1</h6>", unsafe_allow_html=True)
+
+    #pathString = "/Users/660565/KPSAllInOne/ProgramFilesX86/WebCopy/DownloadedWebSites/LLMPOC_HTML" 
+    pathString = "/app/inputDocs"
+    chunks = []
+    webpageDocNames = []
+    page_contentArray = []
+    webpageChunks = []
+    webpageTitles = []
+    webpageChunksDocNames = []
+
+    ######################################################
+    # Connect to the Weaviate vector database.
+    #if 'client' not in st.session_state:
+    logger.info("#### Create Weaviate db client connection.")
+    client = weaviate.WeaviateClient(
+        connection_params=ConnectionParams.from_params(
+            http_host="localhost",
+            http_port="8080",
+            http_secure=False,
+            grpc_host="localhost",
+            grpc_port="50051",
+            grpc_secure=False
+        ),
+        additional_config=AdditionalConfig(
+            timeout=Timeout(init=60, query=1800, insert=1800),  # Values in seconds
+        )
     )
-)
-client.connect()
-#    st.session_state.client = client
-#else:
-#    client = st.session_state.client
-
-
-#######################################################
-# Read each text input file, parse it into a document,
-# chunk it, collect chunks and document name.
-if not client.collections.exists("Documents") or not client.collections.exists("Documents") :
-    logger.info("#### Read and chunk input text files.")
-    for filename in os.listdir(pathString):
-        logger.info(filename)
-        path = Path(pathString + "/" + filename)
-        filename = filename.rstrip(".html")
-        webpageDocNames.append(filename)
-        htmlLoader = BSHTMLLoader(path,"utf-8") 
-        htmlData = htmlLoader.load()
-
-        title   = htmlData[0].metadata['title']
-        page_content = htmlData[0].page_content
-        
-        # Clean data. Remove multiple newlines, etc.
-        page_content = re.sub(r'\n+', '\n',page_content)
-        
-        page_contentArray.append(page_content);
-        webpageTitles.append(title)
-        max_tokens = 1000
-        tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
-        logger.debug(f"### tokenizer: {tokenizer}")
-        splitter = HuggingFaceTextSplitter(tokenizer, trim_chunks=True)
-        chunksOnePage = splitter.chunks(page_content, chunk_capacity=50)
-
-        chunks = []
-        for chnk in chunksOnePage:
-            logger.debug(f"#### chnk in file: {chnk}")
-            chunks.append(chnk)
-            logger.debug(f"chunks: {chunks}")
-        webpageChunks.append(chunks)
-        webpageChunksDocNames.append(filename + "Chunks")
-
-        logger.debug(f"### filename, title: {filename}, {title}")
-        logger.debug(f"### webpageDocNames: {webpageDocNames}")
-
-
-
-######################################################
-# Create database webpage and chunks collections.
-#wpCollection = createWebpageCollection()
-#wpChunkCollection = createChunksCollection()
-if not client.collections.exists("Documents"):
-    logger.info("#### createWebpageCollection() entered.")
-    #client.collections.delete("Documents")
-    class_obj = {
-      "class": "Documents",
-      "description": "For first attempt at loading a Weviate database.",
-      "vectorizer": "text2vec-transformers",
-      "moduleConfig": {
-        "text2vec-transformers": {
-        "vectorizeClassName": False
-        }
-      },
-      "vectorIndexType": "hnsw",  
-      "vectorIndexConfig": {
-          "distance": "cosine",
-      },
-      "properties": [
-            {
-                "name": "title",
-                "dataType": ["text"],
-                "description": "HTML doc title.",
-                "vectorizer": "text2vec-transformers",
-                "moduleConfig": {
-                    "text2vec-transformers": {
-                        "vectorizePropertyName": True,
-                        "skip": False,
-                        "tokenization": "lowercase"
+    client.connect()
+    #    st.session_state.client = client
+    #else:
+    #    client = st.session_state.client
+
+
+    #######################################################
+    # Read each text input file, parse it into a document,
+    # chunk it, collect chunks and document name.
+    if not client.collections.exists("Documents") or not client.collections.exists("Documents") :
+        logger.info("#### Read and chunk input text files.")
+        for filename in os.listdir(pathString):
+            logger.info(filename)
+            path = Path(pathString + "/" + filename)
+            filename = filename.rstrip(".html")
+            webpageDocNames.append(filename)
+            htmlLoader = BSHTMLLoader(path,"utf-8") 
+            htmlData = htmlLoader.load()
+
+            title   = htmlData[0].metadata['title']
+            page_content = htmlData[0].page_content
+            
+            # Clean data. Remove multiple newlines, etc.
+            page_content = re.sub(r'\n+', '\n',page_content)
+            
+            page_contentArray.append(page_content);
+            webpageTitles.append(title)
+            max_tokens = 1000
+            tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
+            logger.debug(f"### tokenizer: {tokenizer}")
+            splitter = HuggingFaceTextSplitter(tokenizer, trim_chunks=True)
+            chunksOnePage = splitter.chunks(page_content, chunk_capacity=50)
+
+            chunks = []
+            for chnk in chunksOnePage:
+                logger.debug(f"#### chnk in file: {chnk}")
+                chunks.append(chnk)
+                logger.debug(f"chunks: {chunks}")
+            webpageChunks.append(chunks)
+            webpageChunksDocNames.append(filename + "Chunks")
+
+            logger.debug(f"### filename, title: {filename}, {title}")
+            logger.debug(f"### webpageDocNames: {webpageDocNames}")
+
+
+
+    ######################################################
+    # Create database webpage and chunks collections.
+    #wpCollection = createWebpageCollection()
+    #wpChunkCollection = createChunksCollection()
+    if not client.collections.exists("Documents"):
+        logger.info("#### createWebpageCollection() entered.")
+        #client.collections.delete("Documents")
+        class_obj = {
+          "class": "Documents",
+          "description": "For first attempt at loading a Weviate database.",
+          "vectorizer": "text2vec-transformers",
+          "moduleConfig": {
+            "text2vec-transformers": {
+            "vectorizeClassName": False
+            }
+          },
+          "vectorIndexType": "hnsw",  
+          "vectorIndexConfig": {
+              "distance": "cosine",
+          },
+          "properties": [
+                {
+                    "name": "title",
+                    "dataType": ["text"],
+                    "description": "HTML doc title.",
+                    "vectorizer": "text2vec-transformers",
+                    "moduleConfig": {
+                        "text2vec-transformers": {
+                            "vectorizePropertyName": True,
+                            "skip": False,
+                            "tokenization": "lowercase"
+                        }
+                    },
+                    "invertedIndexConfig": {
+                        "bm25": {
+                            "b": 0.75,
+                            "k1": 1.2
+                        },
                     }
                 },
-                "invertedIndexConfig": {
-                    "bm25": {
-                        "b": 0.75,
-                        "k1": 1.2
-                    },
-                }
-            },
-            {
-                "name": "content",
-                "dataType": ["text"],
-                "description": "HTML page content.",
-                "moduleConfig": {
-                    "text2vec-transformers": {
-                        "vectorizePropertyName": True,
-                        "tokenization": "whitespace"
+                {
+                    "name": "content",
+                    "dataType": ["text"],
+                    "description": "HTML page content.",
+                    "moduleConfig": {
+                        "text2vec-transformers": {
+                            "vectorizePropertyName": True,
+                            "tokenization": "whitespace"
+                        }
                     }
                 }
-            }
-        ]  
-    }
-    wpCollection = client.collections.create_from_dict(class_obj)
-
-
-if not client.collections.exists("Chunks"):
-    logger.info("#### createChunksCollection() entered.")
-    #client.collections.delete("Chunks")
-    class_obj = {
-        "class": "Chunks",
-        "description": "Collection for document chunks.",
-        "vectorizer": "text2vec-transformers",
-        "moduleConfig": {
-            "text2vec-transformers": {
-                "vectorizeClassName": True
-            }
-        },
-        "vectorIndexType": "hnsw",  
-        "vectorIndexConfig": {
-            "distance": "cosine",
-        },
-        "properties": [
-            {
-                "name": "chunk",
-                "dataType": ["text"],
-                "description": "Single webpage chunk.",
-                "vectorizer": "text2vec-transformers",
-                "moduleConfig": {
-                    "text2vec-transformers": {
-                      "vectorizePropertyName": False,
-                      "skip": False,
-                      "tokenization": "lowercase"
-                    }
+            ]  
+        }
+        wpCollection = client.collections.create_from_dict(class_obj)
+
+
+    if not client.collections.exists("Chunks"):
+        logger.info("#### createChunksCollection() entered.")
+        #client.collections.delete("Chunks")
+        class_obj = {
+            "class": "Chunks",
+            "description": "Collection for document chunks.",
+            "vectorizer": "text2vec-transformers",
+            "moduleConfig": {
+                "text2vec-transformers": {
+                    "vectorizeClassName": True
                 }
             },
-            {
-                "name": "chunk_index",
-                "dataType": ["int"]
+            "vectorIndexType": "hnsw",  
+            "vectorIndexConfig": {
+                "distance": "cosine",
             },
-            {
-                "name": "webpage",
-                "dataType": ["Documents"],
-                "description": "Webpage content chunks.",
-            
-                "invertedIndexConfig": {
-                    "bm25": {
-                        "b": 0.75,
-                        "k1": 1.2
+            "properties": [
+                {
+                    "name": "chunk",
+                    "dataType": ["text"],
+                    "description": "Single webpage chunk.",
+                    "vectorizer": "text2vec-transformers",
+                    "moduleConfig": {
+                        "text2vec-transformers": {
+                          "vectorizePropertyName": False,
+                          "skip": False,
+                          "tokenization": "lowercase"
+                        }
                     }
-                }  
-            }
-        ]
-    }
-    wpChunkCollection = client.collections.create_from_dict(class_obj)
-
-
-###########################################################
-# Create document and chunks objects in the database.
-if not client.collections.exists("Documents") :
-    logger.info("#### Create page/doc db objects.")
-    for i, className in enumerate(webpageDocNames):
-        title = webpageTitles[i]
-        logger.debug(f"## className, title: {className}, {title}")
-        # Create Webpage Object
-        page_content = page_contentArray[i]
-        # Insert the document.
-        wpCollectionObj_uuid = wpCollection.data.insert(
-          {
-            "name": className,
-            "title": title,
-            "content": page_content
-          }
+                },
+                {
+                    "name": "chunk_index",
+                    "dataType": ["int"]
+                },
+                {
+                    "name": "webpage",
+                    "dataType": ["Documents"],
+                    "description": "Webpage content chunks.",
+                
+                    "invertedIndexConfig": {
+                        "bm25": {
+                            "b": 0.75,
+                            "k1": 1.2
+                        }
+                    }  
+                }
+            ]
+        }
+        wpChunkCollection = client.collections.create_from_dict(class_obj)
+
+
+    ###########################################################
+    # Create document and chunks objects in the database.
+    if not client.collections.exists("Documents") :
+        logger.info("#### Create page/doc db objects.")
+        for i, className in enumerate(webpageDocNames):
+            title = webpageTitles[i]
+            logger.debug(f"## className, title: {className}, {title}")
+            # Create Webpage Object
+            page_content = page_contentArray[i]
+            # Insert the document.
+            wpCollectionObj_uuid = wpCollection.data.insert(
+              {
+                "name": className,
+                "title": title,
+                "content": page_content
+              }
+            )
+        
+    if not client.collections.exists("Chunks") :
+        logger.info("#### Create chunk db objects.")
+        # Insert the chunks for the document.
+        for i2, chunk in enumerate(webpageChunks[i]):
+            chunk_uuid = wpChunkCollection.data.insert(
+              {
+                "title": title,
+                "chunk": chunk,
+                "chunk_index": i2,
+                "references":
+                {
+                  "webpage": wpCollectionObj_uuid
+                }
+              }
+            )
+
+
+    #################################################################
+    # Initialize the LLM.
+    model_path = "/app/llama-2-7b-chat.Q4_0.gguf"
+    if 'llm' not in st.session_state:
+        llm = Llama(model_path,
+                    #*,
+                    n_gpu_layers=0,
+                    split_mode=llama_cpp.LLAMA_SPLIT_MODE_LAYER,
+                    main_gpu=0,
+                    tensor_split=None,
+                    vocab_only=False,
+                    use_mmap=True,
+                    use_mlock=False,
+                    kv_overrides=None,
+                    seed=llama_cpp.LLAMA_DEFAULT_SEED,
+                    n_ctx=512,
+                    n_batch=512,
+                    n_threads=8,
+                    n_threads_batch=16,
+                    rope_scaling_type=llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
+                    pooling_type=llama_cpp.LLAMA_POOLING_TYPE_UNSPECIFIED,
+                    rope_freq_base=0.0,
+                    rope_freq_scale=0.0,
+                    yarn_ext_factor=-1.0,
+                    yarn_attn_factor=1.0,
+                    yarn_beta_fast=32.0,
+                    yarn_beta_slow=1.0,
+                    yarn_orig_ctx=0,
+                    logits_all=False,
+                    embedding=False,
+                    offload_kqv=True,
+                    last_n_tokens_size=64,
+                    lora_base=None,
+                    lora_scale=1.0,
+                    lora_path=None,
+                    numa=False,
+                    chat_format=None,
+                    chat_handler=None,
+                    draft_model=None,
+                    tokenizer=None,
+                    type_k=None,
+                    type_v=None,
+                    verbose=True
+                   )
+        st.session_state.llm = llm
+    else:
+        llm = st.session_state.llm
+
+    def getRagData(promptText):
+        logger.info("#### getRagData() entered.")
+        ###############################################################################
+        # Initial the the sentence transformer and encode the query prompt.
+        logger.info(f"#### Encode text query prompt to create vectors. {text}")
+        model = SentenceTransformer('/app/multi-qa-MiniLM-L6-cos-v1')
+
+        vector = model.encode(promptText)
+        vectorList = []
+
+        logger.debug("#### Print vectors.")
+        for vec in vector:
+            vectorList.append(vec)
+        logger.debug(f"vectorList: {vectorList[2]}")
+
+        # Fetch chunks and print chunks.
+        logger.info("#### Retrieve semchunks from db using vectors from prompt.")
+        semChunks = wpChunkCollection.query.near_vector(
+            near_vector=vectorList,
+            distance=0.7,
+            limit=3
         )
-    
-if not client.collections.exists("Chunks") :
-    logger.info("#### Create chunk db objects.")
-    # Insert the chunks for the document.
-    for i2, chunk in enumerate(webpageChunks[i]):
-        chunk_uuid = wpChunkCollection.data.insert(
-          {
-            "title": title,
-            "chunk": chunk,
-            "chunk_index": i2,
-            "references":
-            {
-              "webpage": wpCollectionObj_uuid
-            }
-          }
+        logger.debug(f"### semChunks[0]: {semChunks}")
+
+        # Print chunks, corresponding document and document title.
+        ragData = ""
+        logger.info("#### Print individual retrieved chunks.")
+        for chunk in enumerate(semChunks.objects):
+            logger.info(f"#### chunk: {chunk}")
+            ragData = ragData + "\n" + chunk[0]
+            webpage_uuid = chunk[1].properties['references']['webpage']
+            logger.info(f"webpage_uuid: {webpage_uuid}")
+            wpFromChunk = wpCollection.query.fetch_object_by_id(webpage_uuid)
+            logger.info(f"### wpFromChunk title: {wpFromChunk.properties['title']}")
+        #collection = client.collections.get("Chunks")
+        return  ragData
+
+
+    # Display UI
+    col1, col2 = st.columns(2)
+
+    with col1:
+        if "sysTA" not in st.session_state:
+            st.session_state.sysTA = st.text_area(label="sysTA",value="fdsaf fsdafdsa")
+        elif "sysTAtext" in st.session_state:
+            st.session_state.sysTA = st.text_area(label="sysTA",value=st.session_state.sysTAtext)
+        else:
+            st.session_state.sysTA = st.text_area(label="sysTA",value=st.session_state.sysTA)
+            
+        if "userpTA" not in st.session_state:
+            userTextArea = st.text_area(label="userpTA",value="fdsaf fsdafdsa")
+        elif "userpTAtext" in st.session_state:
+            st.session_state.userpTA = st.text_area(label="userpTA",value=st.session_state.userpTAtext)
+        else:
+            st.session_state.userpTA = st.text_area(label="userpTA",value=st.session_state.userpTA)
+
+    with col2:
+        if "ragpTA" not in st.session_state:
+            ragPromptTextArea = st.text_area(label="ragpTA",value="fdsaf fsdafdsa")
+        elif "ragpTAtext" in st.session_state:
+            st.session_state.ragpTA = st.text_area(label="ragpTA",value=st.session_state.ragpTAtext)
+        else:
+            st.session_state.ragTA = st.text_area(label="ragTA",value=st.session_state.ragTA)
+
+        if "rspTA" not in st.session_state:
+            responseTextArea = st.text_area(label="rspTA",value="fdsaf fsdafdsa")
+        elif "rspTAtext" in st.session_state:
+            st.session_state.rspTA = st.text_area(label="rspTA",value=st.session_state.rspTAtext)
+        else:
+            st.session_state.rspTA = st.text_area(label="rspTA",value=st.session_state.rspTA)
+
+    def runLLM(prompt):
+        max_tokens = 1000
+        temperature = 0.3
+        top_p = 0.1
+        echo = True
+        stop = ["Q", "\n"]
+
+        modelOutput = llm(
+           prompt,
+           max_tokens=max_tokens,
+           temperature=temperature,
+           top_p=top_p,
+           echo=echo,
+           stop=stop,
         )
-
-
-#################################################################
-# Initialize the LLM.
-model_path = "/app/llama-2-7b-chat.Q4_0.gguf"
-if 'llm' not in st.session_state:
-    llm = Llama(model_path,
-                #*,
-                n_gpu_layers=0,
-                split_mode=llama_cpp.LLAMA_SPLIT_MODE_LAYER,
-                main_gpu=0,
-                tensor_split=None,
-                vocab_only=False,
-                use_mmap=True,
-                use_mlock=False,
-                kv_overrides=None,
-                seed=llama_cpp.LLAMA_DEFAULT_SEED,
-                n_ctx=512,
-                n_batch=512,
-                n_threads=8,
-                n_threads_batch=16,
-                rope_scaling_type=llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
-                pooling_type=llama_cpp.LLAMA_POOLING_TYPE_UNSPECIFIED,
-                rope_freq_base=0.0,
-                rope_freq_scale=0.0,
-                yarn_ext_factor=-1.0,
-                yarn_attn_factor=1.0,
-                yarn_beta_fast=32.0,
-                yarn_beta_slow=1.0,
-                yarn_orig_ctx=0,
-                logits_all=False,
-                embedding=False,
-                offload_kqv=True,
-                last_n_tokens_size=64,
-                lora_base=None,
-                lora_scale=1.0,
-                lora_path=None,
-                numa=False,
-                chat_format=None,
-                chat_handler=None,
-                draft_model=None,
-                tokenizer=None,
-                type_k=None,
-                type_v=None,
-                verbose=True
-               )
-    st.session_state.llm = llm
-else:
-    llm = st.session_state.llm
-
-def getRagData(promptText):
-    logger.info("#### getRagData() entered.")
-    ###############################################################################
-    # Initial the the sentence transformer and encode the query prompt.
-    logger.info(f"#### Encode text query prompt to create vectors. {text}")
-    model = SentenceTransformer('/app/multi-qa-MiniLM-L6-cos-v1')
-
-    vector = model.encode(promptText)
-    vectorList = []
-
-    logger.debug("#### Print vectors.")
-    for vec in vector:
-        vectorList.append(vec)
-    logger.debug(f"vectorList: {vectorList[2]}")
-
-    # Fetch chunks and print chunks.
-    logger.info("#### Retrieve semchunks from db using vectors from prompt.")
-    semChunks = wpChunkCollection.query.near_vector(
-        near_vector=vectorList,
-        distance=0.7,
-        limit=3
-    )
-    logger.debug(f"### semChunks[0]: {semChunks}")
-
-    # Print chunks, corresponding document and document title.
-    ragData = ""
-    logger.info("#### Print individual retrieved chunks.")
-    for chunk in enumerate(semChunks.objects):
-        logger.info(f"#### chunk: {chunk}")
-        ragData = ragData + "\n" + chunk[0]
-        webpage_uuid = chunk[1].properties['references']['webpage']
-        logger.info(f"webpage_uuid: {webpage_uuid}")
-        wpFromChunk = wpCollection.query.fetch_object_by_id(webpage_uuid)
-        logger.info(f"### wpFromChunk title: {wpFromChunk.properties['title']}")
-    #collection = client.collections.get("Chunks")
-    return  ragData
-
-
-# Display UI
-col1, col2 = st.columns(2)
-
-with col1:
-    if "sysTA" not in st.session_state:
-        st.session_state.sysTA = st.text_area(label="sysTA",value="fdsaf fsdafdsa")
-    elif "sysTAtext" in st.session_state:
-        st.session_state.sysTA = st.text_area(label="sysTA",value=st.session_state.sysTAtext)
-    else:
-        st.session_state.sysTA = st.text_area(label="sysTA",value=st.session_state.sysTA)
+        result = modelOutput["choices"][0]["text"].strip()
+        return(result)
+
+    def setPrompt(pprompt,ragFlag):
+        logger(f"\n### setPrompt() entered. ragFlag: {ragFlag}")
+        if ragFlag:
+            ragPrompt = getRagData(pprompt)
+            userPrompt = pprompt + "\n" + ragPrompt
+            prompt = userPrompt
+            userPrompt = "Using this information: " + ragPrompt \
+                 + "process the following statement or question and produce a a response" \
+                 + intialPrompt
+        else:
+            userPrompt = pprompt
+        #prompt = f""" <s> [INST] <<SYS>> {systemTextArea.value} </SYS>> Q: {userPrompt} A: [/INST]"""
+        return userPrompt
+
+
+    def on_submitButton_clicked():
+        logger = st.session_state.logger
+        logger.debug("\n### on_submitButton_clicked")
+        st.session_state.sysTAtext = st.session_state.sysTA
+        logger.info(f"sysTAtext: {st.session_state.sysTAtext}")
         
-    if "userpTA" not in st.session_state:
-        userTextArea = st.text_area(label="userpTA",value="fdsaf fsdafdsa")
-    elif "userpTAtext" in st.session_state:
-        st.session_state.userpTA = st.text_area(label="userpTA",value=st.session_state.userpTAtext)
-    else:
-        st.session_state.userpTA = st.text_area(label="userpTA",value=st.session_state.userpTA)
+        st.session_state.userpTAtext = setPrompt("","")
+        st.session_state.userpTA = st.session_state.userpTAtext
+        logger.info(f"userpTAtext: {st.session_state.userpTAtext}")
+        
+        st.session_state.rspTAtext = runLLM(st.session_state.userpTAtext)
+        st.session_state.rspTA = st.session_state.rspTAtext
+        logger.info(f"rspTAtext: {st.session_state.rspTAtext}")
 
-with col2:
-    if "ragpTA" not in st.session_state:
-        ragPromptTextArea = st.text_area(label="ragpTA",value="fdsaf fsdafdsa")
-    elif "ragpTAtext" in st.session_state:
-        st.session_state.ragpTA = st.text_area(label="ragpTA",value=st.session_state.ragpTAtext)
-    else:
-        st.session_state.ragTA = st.text_area(label="ragTA",value=st.session_state.ragTA)
 
-    if "rspTA" not in st.session_state:
-        responseTextArea = st.text_area(label="rspTA",value="fdsaf fsdafdsa")
-    elif "rspTAtext" in st.session_state:
-        st.session_state.rspTA = st.text_area(label="rspTA",value=st.session_state.rspTAtext)
-    else:
-        st.session_state.rspTA = st.text_area(label="rspTA",value=st.session_state.rspTA)
-
-def runLLM(prompt):
-    max_tokens = 1000
-    temperature = 0.3
-    top_p = 0.1
-    echo = True
-    stop = ["Q", "\n"]
-
-    modelOutput = llm(
-       prompt,
-       max_tokens=max_tokens,
-       temperature=temperature,
-       top_p=top_p,
-       echo=echo,
-       stop=stop,
-    )
-    result = modelOutput["choices"][0]["text"].strip()
-    return(result)
-
-def setPrompt(pprompt,ragFlag):
-    logger(f"\n### setPrompt() entered. ragFlag: {ragFlag}")
-    if ragFlag:
-        ragPrompt = getRagData(pprompt)
-        userPrompt = pprompt + "\n" + ragPrompt
-        prompt = userPrompt
-        userPrompt = "Using this information: " + ragPrompt \
-             + "process the following statement or question and produce a a response" \
-             + intialPrompt
-    else:
-        userPrompt = pprompt
-    #prompt = f""" <s> [INST] <<SYS>> {systemTextArea.value} </SYS>> Q: {userPrompt} A: [/INST]"""
-    return userPrompt
-
-
-def on_submitButton_clicked():
-    logger = st.session_state.logger
-    logger.debug("\n### on_submitButton_clicked")
-    st.session_state.sysTAtext = st.session_state.sysTA
-    logger.info(f"sysTAtext: {st.session_state.sysTAtext}")
-    
-    st.session_state.userpTAtext = setPrompt("","")
-    st.session_state.userpTA = st.session_state.userpTAtext
-    logger.info(f"userpTAtext: {st.session_state.userpTAtext}")
-    
-    st.session_state.rspTAtext = runLLM(st.session_state.userpTAtext)
-    st.session_state.rspTA = st.session_state.rspTAtext
-    logger.info(f"rspTAtext: {st.session_state.rspTAtext}")
-
-
-with st.sidebar:
-    st.selectRag    = st.checkbox("Enable Query With RAG",value=False,key="selectRag",help=None,on_change=None,args=None,kwargs=None,disabled=False,label_visibility="visible")
-    st.submitButton = st.button("Run LLM Query",key=None,help=None,on_click=on_submitButton_clicked,args=None,kwargs=None,type="secondary",disabled=False,use_container_width=False)
-
-logger.info("#### semsearch.py terminating.")
+    with st.sidebar:
+        st.selectRag    = st.checkbox("Enable Query With RAG",value=False,key="selectRag",help=None,on_change=None,args=None,kwargs=None,disabled=False,label_visibility="visible")
+        st.submitButton = st.button("Run LLM Query",key=None,help=None,on_click=on_submitButton_clicked,args=None,kwargs=None,type="secondary",disabled=False,use_container_width=False)
+
+    logger.info("#### semsearch.py terminating.")
+except Exception as e:
+    logger.error("#### EXCEPTION. e: {str(e)})
+    with open("/app/startup.log", "r") as file:
+        content = file.read()
+        print(content)