Spaces:
Sleeping
Sleeping
File size: 5,026 Bytes
683edfb cfbd391 119dc78 cfbd391 1f79641 cfbd391 683edfb cfbd391 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
import os
import streamlit as st
import pandas as pd
from datasets import load_dataset
import time
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_csv_agent
from langchain_openai import ChatOpenAI
import ast
# Streamlit App Title and Description
st.title("Patent Data Analysis with LangChain")
st.write("""This app allows you to analyze patent-related datasets interactively using LangChain agents. You can upload datasets, load from Hugging Face, or use a repository directory dataset.""")
# Dataset loading without caching to support progress bar
def load_huggingface_dataset(dataset_name):
# Initialize progress bar
progress_bar = st.progress(0)
try:
# Incrementally update progress
progress_bar.progress(10)
dataset = load_dataset(dataset_name, name="sample", split="train", trust_remote_code=True, uniform_split=True)
progress_bar.progress(50)
if hasattr(dataset, "to_pandas"):
df = dataset.to_pandas()
else:
df = pd.DataFrame(dataset)
progress_bar.progress(100) # Final update to 100%
return df
except Exception as e:
progress_bar.progress(0) # Reset progress bar on failure
raise e
def load_uploaded_csv(uploaded_file):
# Initialize progress bar
progress_bar = st.progress(0)
try:
# Simulate progress
progress_bar.progress(10)
time.sleep(1) # Simulate file processing delay
progress_bar.progress(50)
df = pd.read_csv(uploaded_file)
progress_bar.progress(100) # Final update
return df
except Exception as e:
progress_bar.progress(0) # Reset progress bar on failure
raise e
# Dataset selection logic
def load_dataset_into_session():
input_option = st.radio(
"Select Dataset Input:",
["Use Repo Directory Dataset", "Use Hugging Face Dataset", "Upload CSV File"], index=1, horizontal=True
)
# Option 1: Load dataset from the repo directory
if input_option == "Use Repo Directory Dataset":
file_path = "./source/test.csv"
if st.button("Load Dataset"):
try:
with st.spinner("Loading dataset from the repo directory..."):
st.session_state.df = pd.read_csv(file_path)
st.success(f"File loaded successfully from '{file_path}'!")
except Exception as e:
st.error(f"Error loading dataset from the repo directory: {e}")
# Option 2: Load dataset from Hugging Face
elif input_option == "Use Hugging Face Dataset":
dataset_name = st.text_input(
"Enter Hugging Face Dataset Name:", value="HUPD/hupd"
)
if st.button("Load Dataset"):
try:
st.session_state.df = load_huggingface_dataset(dataset_name)
st.success(f"Hugging Face Dataset '{dataset_name}' loaded successfully!")
except Exception as e:
st.error(f"Error loading Hugging Face dataset: {e}")
# Option 3: Upload CSV File
elif input_option == "Upload CSV File":
uploaded_file = st.file_uploader("Upload a CSV File:", type=["csv"])
if uploaded_file:
try:
st.session_state.df = load_uploaded_csv(uploaded_file)
st.success("File uploaded successfully!")
except Exception as e:
st.error(f"Error reading uploaded file: {e}")
# Load dataset into session
load_dataset_into_session()
if "df" in st.session_state:
df = st.session_state.df
# Display dataset metadata
st.write("### Dataset Metadata")
st.text(f"Number of Rows: {df.shape[0]}")
st.text(f"Number of Columns: {df.shape[1]}")
st.text(f"Column Names: {', '.join(df.columns)}")
# Display dataset preview
st.write("### Dataset Preview")
num_rows = st.slider("Select number of rows to display:", min_value=5, max_value=50, value=10)
st.dataframe(df.head(num_rows))
# Define LangChain CSV Agent
st.header("Run Queries on Patent Data")
with st.spinner("Setting up LangChain CSV Agent..."):
df.to_csv("patent_data.csv", index=False)
csv_agent = create_csv_agent(
ChatOpenAI(temperature=0, model="gpt-4", api_key=os.getenv("OPENAI_API_KEY")),
path=["patent_data.csv"],
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
allow_dangerous_code=True
)
# Query Input and Execution
query = st.text_area("Enter your natural language query:", "How many patents are related to AI?")
if st.button("Run Query"):
with st.spinner("Running query..."):
try:
result = csv_agent.invoke(query)
st.success("Query executed successfully!")
st.write("### Query Result:")
st.write(result)
except Exception as e:
st.error(f"Error executing query: {e}")
|