Spaces:
Running
Running
MVPilgrim
commited on
Commit
·
7fea130
1
Parent(s):
13aea45
debug
Browse files
app.py
CHANGED
@@ -27,7 +27,7 @@ try:
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weaviate_logger = logging.getLogger("httpx")
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weaviate_logger.setLevel(logging.WARNING)
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logger = logging.getLogger(__name__)
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-
logging.basicConfig(level=logging.
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st.session_state.weaviate_logger = weaviate_logger
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st.session_state.logger = logger
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else:
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@@ -36,7 +36,7 @@ try:
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def runStartup():
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-
logger.
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result = ""
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try:
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#result = subprocess.run("/app/startup.sh",shell=False,capture_output=None,text=None,timeout=300)
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@@ -47,15 +47,15 @@ try:
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time.sleep(180)
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except Exception as e:
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emsg = str(e)
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-
logger.
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try:
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with open("/app/startup.log", "r") as file:
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content = file.read()
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print(content)
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except Exception as e2:
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emsg = str(e2)
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-
logger.
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-
logger.
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if 'runStartup' not in st.session_state:
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st.session_state.runStartup = True
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runStartup()
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@@ -65,14 +65,14 @@ try:
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######################################################################
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# MAINLINE
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#
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-
logger.
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# Function to load the CSS file
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def load_css(file_name):
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-
logger.
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with open(file_name) as f:
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st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
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-
logger.
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# Load the custom CSS
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if 'load_css' not in st.session_state:
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@@ -95,7 +95,7 @@ try:
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# Connect to the Weaviate vector database.
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#if 'client' not in st.session_state:
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if 'client' not in st.session_state:
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-
logger.
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client = weaviate.WeaviateClient(
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connection_params=ConnectionParams.from_params(
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http_host="localhost",
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@@ -111,7 +111,7 @@ try:
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)
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client.connect()
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st.session_state.client = client
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-
logger.
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else:
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client = st.session_state.client
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@@ -120,9 +120,9 @@ try:
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# Read each text input file, parse it into a document,
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# chunk it, collect chunks and document name.
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if not client.collections.exists("Documents") or not client.collections.exists("Chunks") :
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-
logger.
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for filename in os.listdir(pathString):
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-
logger.
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path = Path(pathString + "/" + filename)
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filename = filename.rstrip(".html")
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webpageDocNames.append(filename)
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@@ -139,21 +139,21 @@ try:
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webpageTitles.append(title)
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max_tokens = 1000
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tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
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-
logger.
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splitter = HuggingFaceTextSplitter(tokenizer, trim_chunks=True)
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chunksOnePage = splitter.chunks(page_content, chunk_capacity=50)
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chunks = []
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for chnk in chunksOnePage:
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-
logger.
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chunks.append(chnk)
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-
logger.
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webpageChunks.append(chunks)
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webpageChunksDocNames.append(filename + "Chunks")
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-
logger.
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-
logger.
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-
logger.
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@@ -162,7 +162,7 @@ try:
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#wpCollection = createWebpageCollection()
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#wpChunkCollection = createChunksCollection()
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if not client.collections.exists("Documents"):
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-
logger.
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#client.collections.delete("Documents")
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class_obj = {
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"class": "Documents",
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@@ -211,11 +211,11 @@ try:
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]
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}
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wpCollection = client.collections.create_from_dict(class_obj)
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-
logger.
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if not client.collections.exists("Chunks"):
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-
logger.
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#client.collections.delete("Chunks")
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class_obj = {
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"class": "Chunks",
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@@ -263,16 +263,16 @@ try:
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]
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}
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wpChunkCollection = client.collections.create_from_dict(class_obj)
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-
logger.
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###########################################################
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# Create document and chunks objects in the database.
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if not client.collections.exists("Documents") :
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-
logger.
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for i, className in enumerate(webpageDocNames):
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title = webpageTitles[i]
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-
logger.
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# Create Webpage Object
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page_content = page_contentArray[i]
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# Insert the document.
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@@ -283,10 +283,10 @@ try:
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"content": page_content
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}
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)
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-
logger.
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if not client.collections.exists("Chunks") :
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-
logger.
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# Insert the chunks for the document.
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for i2, chunk in enumerate(webpageChunks[i]):
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chunk_uuid = wpChunkCollection.data.insert(
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@@ -300,14 +300,14 @@ try:
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}
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}
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)
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-
logger.
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#################################################################
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# Initialize the LLM.
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model_path = "/app/llama-2-7b-chat.Q4_0.gguf"
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if 'llm' not in st.session_state:
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-
logger.
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llm = Llama(model_path,
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#*,
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n_gpu_layers=0,
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@@ -349,45 +349,46 @@ try:
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verbose=True
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)
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st.session_state.llm = llm
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-
logger.
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else:
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llm = st.session_state.llm
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def getRagData(promptText):
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-
logger.
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###############################################################################
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# Initial the the sentence transformer and encode the query prompt.
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-
logger.
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model = SentenceTransformer('/app/multi-qa-MiniLM-L6-cos-v1')
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vector = model.encode(promptText)
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vectorList = []
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-
logger.
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for vec in vector:
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vectorList.append(vec)
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-
logger.
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# Fetch chunks and print chunks.
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-
logger.
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semChunks = wpChunkCollection.query.near_vector(
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near_vector=vectorList,
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distance=0.7,
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limit=3
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)
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-
logger.
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# Print chunks, corresponding document and document title.
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ragData = ""
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-
logger.
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for chunk in enumerate(semChunks.objects):
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-
logger.
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ragData = ragData + "\n" + chunk[0]
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webpage_uuid = chunk[1].properties['references']['webpage']
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-
logger.
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wpFromChunk = wpCollection.query.fetch_object_by_id(webpage_uuid)
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-
logger.
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#collection = client.collections.get("Chunks")
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return ragData
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@@ -426,7 +427,7 @@ try:
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def runLLM(prompt):
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logger = st.session_state.logger
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-
logger.
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max_tokens = 1000
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temperature = 0.3
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@@ -443,7 +444,8 @@ try:
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stop=stop,
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)
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result = modelOutput["choices"][0]["text"].strip()
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-
logger.
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return(result)
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def setPrompt(pprompt,ragFlag):
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@@ -459,14 +461,14 @@ try:
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else:
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userPrompt = pprompt
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#prompt = f""" <s> [INST] <<SYS>> {systemTextArea.value} </SYS>> Q: {userPrompt} A: [/INST]"""
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-
logger.
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return userPrompt
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def on_submitButton_clicked():
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logger = st.session_state.logger
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-
logger.
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-
logger.debug("\n### on_submitButton_clicked")
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st.session_state.sysTAtext = st.session_state.sysTA
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logger.info(f"sysTAtext: {st.session_state.sysTAtext}")
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@@ -478,7 +480,7 @@ try:
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st.session_state.rspTA = st.session_state.rspTAtext
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logger.info(f"rspTAtext: {st.session_state.rspTAtext}")
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-
logger.
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with st.sidebar:
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@@ -489,10 +491,10 @@ try:
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except Exception as e:
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try:
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emsg = str(e)
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-
logger.
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with open("/app/startup.log", "r") as file:
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content = file.read()
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-
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except Exception as e2:
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emsg = str(e2)
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-
logger.
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weaviate_logger = logging.getLogger("httpx")
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weaviate_logger.setLevel(logging.WARNING)
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logger = logging.getLogger(__name__)
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+
logging.basicConfig(level=logging.INFO)
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st.session_state.weaviate_logger = weaviate_logger
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st.session_state.logger = logger
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else:
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def runStartup():
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+
logger.INFO("### Running startup.sh")
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result = ""
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try:
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#result = subprocess.run("/app/startup.sh",shell=False,capture_output=None,text=None,timeout=300)
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time.sleep(180)
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except Exception as e:
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emsg = str(e)
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+
logger.ERROR(f"subprocess.run EXCEPTION. e: {emsg}")
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try:
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with open("/app/startup.log", "r") as file:
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content = file.read()
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print(content)
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except Exception as e2:
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emsg = str(e2)
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+
logger.ERROR(f"#### Displaying startup.log EXCEPTION. e2: {emsg}")
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+
logger.INFO("### Running startup.sh complete")
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if 'runStartup' not in st.session_state:
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st.session_state.runStartup = True
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runStartup()
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######################################################################
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# MAINLINE
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#
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+
logger.INFO("#### MAINLINE ENTERED.")
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# Function to load the CSS file
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def load_css(file_name):
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+
logger.INFO("#### load_css entered.")
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with open(file_name) as f:
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st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
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+
logger.INFO("#### load_css exited.")
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# Load the custom CSS
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if 'load_css' not in st.session_state:
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# Connect to the Weaviate vector database.
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#if 'client' not in st.session_state:
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if 'client' not in st.session_state:
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+
logger.INFO("#### Create Weaviate db client connection.")
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client = weaviate.WeaviateClient(
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connection_params=ConnectionParams.from_params(
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http_host="localhost",
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)
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client.connect()
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st.session_state.client = client
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+
logger.INFO("#### Create Weaviate db client connection exited.")
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else:
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client = st.session_state.client
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# Read each text input file, parse it into a document,
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# chunk it, collect chunks and document name.
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if not client.collections.exists("Documents") or not client.collections.exists("Chunks") :
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+
logger.INFO("#### Read and chunk input text files.")
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for filename in os.listdir(pathString):
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+
logger.DEBUG(filename)
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path = Path(pathString + "/" + filename)
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filename = filename.rstrip(".html")
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webpageDocNames.append(filename)
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webpageTitles.append(title)
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max_tokens = 1000
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tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
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142 |
+
logger.DEBUG(f"### tokenizer: {tokenizer}")
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splitter = HuggingFaceTextSplitter(tokenizer, trim_chunks=True)
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chunksOnePage = splitter.chunks(page_content, chunk_capacity=50)
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chunks = []
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for chnk in chunksOnePage:
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+
logger.DEBUG(f"#### chnk in file: {chnk}")
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chunks.append(chnk)
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+
logger.DEBUG(f"chunks: {chunks}")
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webpageChunks.append(chunks)
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webpageChunksDocNames.append(filename + "Chunks")
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+
logger.DEBUG(f"### filename, title: {filename}, {title}")
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+
logger.DEBUG(f"### webpageDocNames: {webpageDocNames}")
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+
logger.INFO("#### Read and chunk input text files exited.")
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#wpCollection = createWebpageCollection()
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#wpChunkCollection = createChunksCollection()
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if not client.collections.exists("Documents"):
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+
logger.INFO("#### createWebpageCollection() entered.")
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#client.collections.delete("Documents")
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class_obj = {
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"class": "Documents",
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211 |
]
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}
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wpCollection = client.collections.create_from_dict(class_obj)
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214 |
+
logger.INFO("#### createWebpageCollection() exited.")
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215 |
|
216 |
|
217 |
if not client.collections.exists("Chunks"):
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218 |
+
logger.INFO("#### createChunksCollection() entered.")
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219 |
#client.collections.delete("Chunks")
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220 |
class_obj = {
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221 |
"class": "Chunks",
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|
263 |
]
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264 |
}
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265 |
wpChunkCollection = client.collections.create_from_dict(class_obj)
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+
logger.INFO("#### createChunksCollection() exited.")
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267 |
|
268 |
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###########################################################
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# Create document and chunks objects in the database.
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if not client.collections.exists("Documents") :
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272 |
+
logger.INFO("#### Create page/doc db objects.")
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273 |
for i, className in enumerate(webpageDocNames):
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274 |
title = webpageTitles[i]
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275 |
+
logger.DEBUG(f"## className, title: {className}, {title}")
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276 |
# Create Webpage Object
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277 |
page_content = page_contentArray[i]
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278 |
# Insert the document.
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283 |
"content": page_content
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284 |
}
|
285 |
)
|
286 |
+
logger.INFO("#### Create page/doc/db/objects exited.")
|
287 |
|
288 |
if not client.collections.exists("Chunks") :
|
289 |
+
logger.INFO("#### Create chunk db objects.")
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290 |
# Insert the chunks for the document.
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291 |
for i2, chunk in enumerate(webpageChunks[i]):
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292 |
chunk_uuid = wpChunkCollection.data.insert(
|
|
|
300 |
}
|
301 |
}
|
302 |
)
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303 |
+
logger.INFO("#### Create chunk db objects exited.")
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304 |
|
305 |
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306 |
#################################################################
|
307 |
# Initialize the LLM.
|
308 |
model_path = "/app/llama-2-7b-chat.Q4_0.gguf"
|
309 |
if 'llm' not in st.session_state:
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310 |
+
logger.INFO("### Initializing LLM.")
|
311 |
llm = Llama(model_path,
|
312 |
#*,
|
313 |
n_gpu_layers=0,
|
|
|
349 |
verbose=True
|
350 |
)
|
351 |
st.session_state.llm = llm
|
352 |
+
logger.INFO("### Initializing LLM exited.")
|
353 |
else:
|
354 |
llm = st.session_state.llm
|
355 |
|
356 |
def getRagData(promptText):
|
357 |
+
logger.INFO("#### getRagData() entered.")
|
358 |
###############################################################################
|
359 |
# Initial the the sentence transformer and encode the query prompt.
|
360 |
+
logger.DEBUG(f"#### Encode text query prompt to create vectors. {text}")
|
361 |
model = SentenceTransformer('/app/multi-qa-MiniLM-L6-cos-v1')
|
362 |
|
363 |
vector = model.encode(promptText)
|
364 |
vectorList = []
|
365 |
|
366 |
+
logger.DEBUG("#### Print vectors.")
|
367 |
for vec in vector:
|
368 |
vectorList.append(vec)
|
369 |
+
logger.DEBUG(f"vectorList: {vectorList[2]}")
|
370 |
|
371 |
# Fetch chunks and print chunks.
|
372 |
+
logger.DEBUG("#### Retrieve semchunks from db using vectors from prompt.")
|
373 |
semChunks = wpChunkCollection.query.near_vector(
|
374 |
near_vector=vectorList,
|
375 |
distance=0.7,
|
376 |
limit=3
|
377 |
)
|
378 |
+
logger.DEBUG(f"### semChunks[0]: {semChunks}")
|
379 |
|
380 |
# Print chunks, corresponding document and document title.
|
381 |
ragData = ""
|
382 |
+
logger.DEBUG("#### Print individual retrieved chunks.")
|
383 |
for chunk in enumerate(semChunks.objects):
|
384 |
+
logger.INFO(f"#### chunk: {chunk}")
|
385 |
ragData = ragData + "\n" + chunk[0]
|
386 |
webpage_uuid = chunk[1].properties['references']['webpage']
|
387 |
+
logger.INFO(f"webpage_uuid: {webpage_uuid}")
|
388 |
wpFromChunk = wpCollection.query.fetch_object_by_id(webpage_uuid)
|
389 |
+
logger.INFO(f"### wpFromChunk title: {wpFromChunk.properties['title']}")
|
390 |
#collection = client.collections.get("Chunks")
|
391 |
+
logger.INFO("#### getRagData() exited.")
|
392 |
return ragData
|
393 |
|
394 |
|
|
|
427 |
|
428 |
def runLLM(prompt):
|
429 |
logger = st.session_state.logger
|
430 |
+
logger.INFO("### runLLM entered.")
|
431 |
|
432 |
max_tokens = 1000
|
433 |
temperature = 0.3
|
|
|
444 |
stop=stop,
|
445 |
)
|
446 |
result = modelOutput["choices"][0]["text"].strip()
|
447 |
+
logger.INFO(f"### llmResult: {result}")
|
448 |
+
logger.INFO("### runLLM exited.")
|
449 |
return(result)
|
450 |
|
451 |
def setPrompt(pprompt,ragFlag):
|
|
|
461 |
else:
|
462 |
userPrompt = pprompt
|
463 |
#prompt = f""" <s> [INST] <<SYS>> {systemTextArea.value} </SYS>> Q: {userPrompt} A: [/INST]"""
|
464 |
+
logger.INFO("setPrompt exited.")
|
465 |
+
logger.INFO(f"### userPrompt: {userPrompt}")
|
466 |
return userPrompt
|
467 |
|
468 |
|
469 |
def on_submitButton_clicked():
|
470 |
logger = st.session_state.logger
|
471 |
+
logger.INFO("### on_submitButton_clicked entered.")
|
|
|
472 |
st.session_state.sysTAtext = st.session_state.sysTA
|
473 |
logger.info(f"sysTAtext: {st.session_state.sysTAtext}")
|
474 |
|
|
|
480 |
st.session_state.rspTA = st.session_state.rspTAtext
|
481 |
logger.info(f"rspTAtext: {st.session_state.rspTAtext}")
|
482 |
|
483 |
+
logger.INFO("### on_submitButton_clicked exited.")
|
484 |
|
485 |
|
486 |
with st.sidebar:
|
|
|
491 |
except Exception as e:
|
492 |
try:
|
493 |
emsg = str(e)
|
494 |
+
logger.ERROR(f"Program-wide EXCEPTION. e: {emsg}")
|
495 |
with open("/app/startup.log", "r") as file:
|
496 |
content = file.read()
|
497 |
+
logger.DEBUG(content)
|
498 |
except Exception as e2:
|
499 |
emsg = str(e2)
|
500 |
+
logger.ERROR(f"#### Displaying startup.log EXCEPTION. e2: {emsg}")
|