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import streamlit as st
import pandas as pd
import os
from langchain.callbacks import StreamlitCallbackHandler
from langchain_groq import ChatGroq
from langchain_experimental.agents import create_pandas_dataframe_agent
from langchain.agents.agent_types import AgentType
from langchain.agents import AgentExecutor, Tool
from langchain.tools.python.tool import PythonAstREPLTool
from dotenv import load_dotenv
load_dotenv()
def clear_submit():
st.session_state["submit"] = False
@st.cache_data(ttl="2h")
def load_data(uploaded_file):
try:
ext = os.path.splitext(uploaded_file.name)[1][1:].lower()
except:
ext = uploaded_file.split(".")[-1]
if ext in ['xls', 'xlsx', 'xlsm', 'xlsb']:
xls = pd.ExcelFile(uploaded_file)
sheet_to_df_map = {}
for sheet_name in xls.sheet_names:
sheet_to_df_map[sheet_name] = pd.read_excel(xls, sheet_name)
return sheet_to_df_map
elif ext == 'csv':
return {"main": pd.read_csv(uploaded_file)}
else:
st.error(f"Unsupported file format: {ext}")
return None
def create_agent_for_dataframes(llm, dfs):
tools = []
for name, df in dfs.items():
tools.append(
Tool(
name=f"python_repl_{name}",
description=f"A Python REPL for interacting with the '{name}' dataframe. Use this to execute python commands. Input should be a valid python command. When using this tool, you can access the dataframe for '{name}' as 'df'.",
func=PythonAstREPLTool(locals={"df": df}).run
)
)
agent = create_pandas_dataframe_agent(
llm,
dfs,
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
handle_parsing_errors=True,
)
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
st.set_page_config(page_title="LangChain: Chat with Excel Data", page_icon="🦜")
st.title("🦜 LangChain: Chat with Excel Data")
uploaded_file = st.file_uploader(
"Upload an Excel or CSV file",
type=['csv', 'xls', 'xlsx', 'xlsm', 'xlsb'],
help="Various File formats are Supported",
on_change=clear_submit,
)
if not uploaded_file:
st.warning(
"This app uses LangChain's `PythonAstREPLTool` which is vulnerable to arbitrary code execution. Please use caution in deploying and sharing this app."
)
if uploaded_file:
dfs = load_data(uploaded_file)
if isinstance(dfs, dict):
st.write(f"Loaded {len(dfs)} table(s) from the file:")
for sheet_name, df in dfs.items():
st.write(f"- {sheet_name}: {df.shape[0]} rows, {df.shape[1]} columns")
llm = ChatGroq(
model="mixtral-8x7b-32768",
temperature=0,
max_tokens=1024,
api_key=os.getenv("GROQ_API_KEY"),
streaming=True
)
agent_executor = create_agent_for_dataframes(llm, dfs)
if "messages" not in st.session_state or st.sidebar.button("Clear conversation history"):
st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you with the data from all tables?"}]
for msg in st.session_state.messages:
st.chat_message(msg["role"]).write(msg["content"])
if prompt := st.chat_input(placeholder="Ask a question about the data..."):
st.session_state.messages.append({"role": "user", "content": prompt})
st.chat_message("user").write(prompt)
with st.chat_message("assistant"):
st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
response = agent_executor.run(prompt, callbacks=[st_cb])
st.session_state.messages.append({"role": "assistant", "content": response})
st.write(response)
else:
st.error("Failed to load the file. Please check the file format and try again.")
####################
import streamlit as st
import pandas as pd
import os
import json
import matplotlib.pyplot as plt
from langchain.callbacks import StreamlitCallbackHandler
from langchain_groq import ChatGroq
from langchain_experimental.agents import create_pandas_dataframe_agent
from langchain.agents.agent_types import AgentType
from langchain.agents import AgentExecutor, Tool
from langchain.tools.python.tool import PythonAstREPLTool
from dotenv import load_dotenv
load_dotenv()
def clear_submit():
st.session_state["submit"] = False
@st.cache_data(ttl="2h")
def load_data(uploaded_file):
try:
ext = os.path.splitext(uploaded_file.name)[1][1:].lower()
except:
ext = uploaded_file.split(".")[-1]
if ext in ['xls', 'xlsx', 'xlsm', 'xlsb']:
xls = pd.ExcelFile(uploaded_file)
sheet_to_df_map = {}
for sheet_name in xls.sheet_names:
sheet_to_df_map[sheet_name] = pd.read_excel(xls, sheet_name)
return sheet_to_df_map
elif ext == 'csv':
return {"main": pd.read_csv(uploaded_file)}
else:
st.error(f"Unsupported file format: {ext}")
return None
def create_json_data(data):
"""Create a JSON string from the provided data."""
return json.dumps(data, indent=2)
def plot_data(df, x_col, y_col, title):
"""Create a simple plot using matplotlib."""
plt.figure(figsize=(10, 6))
plt.plot(df[x_col], df[y_col])
plt.title(title)
plt.xlabel(x_col)
plt.ylabel(y_col)
return plt
def create_agent_for_dataframes(llm, dfs):
tools = []
for name, df in dfs.items():
tools.append(
Tool(
name=f"python_repl_{name}",
description=f"A Python REPL for interacting with the '{name}' dataframe. Use this to execute python commands. Input should be a valid python command. When using this tool, you can access the dataframe for '{name}' as 'df'.",
func=PythonAstREPLTool(locals={"df": df}).run
)
)
# Add JSON creation tool
tools.append(
Tool(
name="create_json",
description="Create a JSON string from provided data. Input should be a dictionary or list.",
func=create_json_data
)
)
# Add plotting tool
tools.append(
Tool(
name="create_plot",
description="Create a plot using matplotlib. Input should be a dictionary with 'df_name', 'x_col', 'y_col', and 'title'.",
func=lambda x: plot_data(dfs[x['df_name']], x['x_col'], x['y_col'], x['title'])
)
)
agent = create_pandas_dataframe_agent(
llm,
dfs,
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
handle_parsing_errors=True,
)
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
st.set_page_config(page_title="LangChain: Chat with Excel Data", page_icon="🦜")
st.title("🦜 LangChain: Chat with Excel Data")
uploaded_file = st.file_uploader(
"Upload an Excel or CSV file",
type=['csv', 'xls', 'xlsx', 'xlsm', 'xlsb'],
help="Various File formats are Supported",
on_change=clear_submit,
)
if not uploaded_file:
st.warning(
"This app uses LangChain's `PythonAstREPLTool` which is vulnerable to arbitrary code execution. Please use caution in deploying and sharing this app."
)
if uploaded_file:
dfs = load_data(uploaded_file)
if isinstance(dfs, dict):
st.write(f"Loaded {len(dfs)} table(s) from the file:")
for sheet_name, df in dfs.items():
st.write(f"- {sheet_name}: {df.shape[0]} rows, {df.shape[1]} columns")
llm = ChatGroq(
model="mixtral-8x7b-32768",
temperature=0,
max_tokens=1024,
api_key=os.getenv("GROQ_API_KEY"),
streaming=True
)
agent_executor = create_agent_for_dataframes(llm, dfs)
if "messages" not in st.session_state or st.sidebar.button("Clear conversation history"):
st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you with the data from all tables? I can now create JSON data and plots as well!"}]
for msg in st.session_state.messages:
st.chat_message(msg["role"]).write(msg["content"])
if prompt := st.chat_input(placeholder="Ask a question about the data..."):
st.session_state.messages.append({"role": "user", "content": prompt})
st.chat_message("user").write(prompt)
with st.chat_message("assistant"):
st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
response = agent_executor.run(prompt, callbacks=[st_cb])
st.session_state.messages.append({"role": "assistant", "content": response})
st.write(response)
# Display plot if one was created
if 'plt' in locals():
st.pyplot(plt)
plt.close()
else:
st.error("Failed to load the file. Please check the file format and try again.")Leave a Comment