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Build error
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3b3c852
1
Parent(s):
f790556
added debugging features
Browse files
app.py
CHANGED
@@ -1,4 +1,5 @@
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from copy import deepcopy
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import streamlit as st
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import pandas as pd
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from io import StringIO
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@@ -8,7 +9,13 @@ import weaviate
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from weaviate.embedded import EmbeddedOptions
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from weaviate import Client
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from weaviate.util import generate_uuid5
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# Initialize TAPAS model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-wtq")
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model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq")
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@@ -18,6 +25,22 @@ client = weaviate.Client(
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embedded_options=EmbeddedOptions()
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)
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# Function to check if a class already exists in Weaviate
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def class_exists(class_name):
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try:
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@@ -76,6 +99,8 @@ def ingest_data_to_weaviate(dataframe, class_name, class_description):
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}
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client.data_object.create(obj)
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def query_weaviate(question):
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# This is a basic example; adapt the query based on the question
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@@ -87,10 +112,12 @@ def ask_llm_chunk(chunk, questions):
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try:
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inputs = tokenizer(table=chunk, queries=questions, padding="max_length", truncation=True, return_tensors="pt")
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except Exception as e:
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st.write(f"An error occurred: {e}")
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return ["Error occurred while tokenizing"] * len(questions)
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if inputs["input_ids"].shape[1] > 512:
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st.warning("Token limit exceeded for chunk")
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return ["Token limit exceeded for chunk"] * len(questions)
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@@ -106,13 +133,11 @@ def ask_llm_chunk(chunk, questions):
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if len(coordinates) == 1:
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row, col = coordinates[0]
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try:
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st.write(f"DataFrame shape: {chunk.shape}") # Debugging line
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st.write(f"DataFrame columns: {chunk.columns}") # Debugging line
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st.write(f"Trying to access row {row}, col {col}") # Debugging line
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value = chunk.iloc[row, col]
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answers.append(value)
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except Exception as e:
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st.write(f"An error occurred: {e}")
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else:
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cell_values = []
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@@ -122,6 +147,7 @@ def ask_llm_chunk(chunk, questions):
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value = chunk.iloc[row, col]
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cell_values.append(value)
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except Exception as e:
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st.write(f"An error occurred: {e}")
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answers.append(", ".join(map(str, cell_values)))
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@@ -180,6 +206,9 @@ if selected_class != "New Class":
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if csv_file is not None:
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data = csv_file.read().decode("utf-8")
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dataframe = pd.read_csv(StringIO(data))
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# Display the uploaded CSV data
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st.write("Uploaded CSV Data:")
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@@ -207,6 +236,12 @@ if csv_file is not None:
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st.write(f"Question: {q}")
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st.write(f"Answer: {a}")
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# Add Ctrl+Enter functionality for submitting the questions
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st.markdown("""
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<script>
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from copy import deepcopy
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from langchain.callbacks import StreamlitCallbackHandler
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import streamlit as st
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import pandas as pd
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from io import StringIO
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from weaviate.embedded import EmbeddedOptions
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from weaviate import Client
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from weaviate.util import generate_uuid5
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import logging
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class StreamlitCallbackHandler(logging.Handler):
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def emit(self, record):
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log_entry = self.format(record)
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st.write(log_entry)
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# Initialize TAPAS model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-wtq")
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model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq")
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embedded_options=EmbeddedOptions()
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)
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# Global list to store debugging information
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DEBUG_LOGS = []
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def log_debug_info(message):
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if st.session_state.debug:
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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# Check if StreamlitCallbackHandler is already added to avoid duplicate logs
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if not any(isinstance(handler, StreamlitCallbackHandler) for handler in logger.handlers):
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handler = StreamlitCallbackHandler()
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logger.addHandler(handler)
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logger.debug(message)
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# Function to check if a class already exists in Weaviate
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def class_exists(class_name):
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try:
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}
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client.data_object.create(obj)
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# Log data ingestion
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log_debug_info(f"Data ingested into Weaviate for class: {class_name}")
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def query_weaviate(question):
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# This is a basic example; adapt the query based on the question
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try:
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inputs = tokenizer(table=chunk, queries=questions, padding="max_length", truncation=True, return_tensors="pt")
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except Exception as e:
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log_debug_info(f"Tokenization error: {e}")
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st.write(f"An error occurred: {e}")
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return ["Error occurred while tokenizing"] * len(questions)
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if inputs["input_ids"].shape[1] > 512:
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log_debug_info("Token limit exceeded for chunk")
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st.warning("Token limit exceeded for chunk")
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return ["Token limit exceeded for chunk"] * len(questions)
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if len(coordinates) == 1:
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row, col = coordinates[0]
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try:
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value = chunk.iloc[row, col]
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log_debug_info(f"Accessed value for row {row}, col {col}: {value}")
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answers.append(value)
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except Exception as e:
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log_debug_info(f"Error accessing value for row {row}, col {col}: {e}")
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st.write(f"An error occurred: {e}")
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else:
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cell_values = []
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value = chunk.iloc[row, col]
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cell_values.append(value)
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except Exception as e:
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log_debug_info(f"Error accessing value for row {row}, col {col}: {e}")
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st.write(f"An error occurred: {e}")
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answers.append(", ".join(map(str, cell_values)))
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if csv_file is not None:
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data = csv_file.read().decode("utf-8")
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dataframe = pd.read_csv(StringIO(data))
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# Log CSV upload information
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log_debug_info(f"CSV uploaded with shape: {dataframe.shape}")
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# Display the uploaded CSV data
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st.write("Uploaded CSV Data:")
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st.write(f"Question: {q}")
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st.write(f"Answer: {a}")
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# Display debugging information
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if st.checkbox("Show Debugging Information"):
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st.write("Debugging Logs:")
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for log in DEBUG_LOGS:
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st.write(log)
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# Add Ctrl+Enter functionality for submitting the questions
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st.markdown("""
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<script>
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