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import streamlit as st
import pandas as pd
import os
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
import tempfile
import matplotlib.pyplot as plt
from datasets import load_dataset
# Load environment variables
openai_api_key = os.getenv("OPENAI_API_KEY")
# Ensure OpenAI API key is provided
if not openai_api_key:
st.error("OpenAI API key is not set. Please add it to a .env file.")
st.stop()
# Initialize the LLM
#llm = OpenAI(api_token=openai_api_key)
def initialize_llm(model_choice):
groq_api_key = os.getenv("GROQ_API_KEY")
openai_api_key = os.getenv("OPENAI_API_KEY")
if model_choice == "llama-3.3-70b":
if not groq_api_key:
st.error("Groq API key is missing. Please set the GROQ_API_KEY environment variable.")
return None
return ChatGroq(groq_api_key=groq_api_key, model="groq/llama-3.3-70b-versatile")
elif model_choice == "GPT-4o":
if not openai_api_key:
st.error("OpenAI API key is missing. Please set the OPENAI_API_KEY environment variable.")
return None
return ChatOpenAI(api_key=openai_api_key, model="gpt-4o")
model_choice = st.radio("Select LLM", ["GPT-4o", "llama-3.3-70b"], index=0, horizontal=True)
llm = initialize_llm(model_choice)
def load_dataset_into_session():
input_option = st.radio(
"Select Dataset Input:",
["Use Repo Directory Dataset", "Use Hugging Face Dataset", "Upload CSV File"],
)
# 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:
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 Hugging Face Dataset"):
try:
dataset = load_dataset(dataset_name, split="train", trust_remote_code=True)
if hasattr(dataset, "to_pandas"):
st.session_state.df = dataset.to_pandas()
else:
st.session_state.df = pd.DataFrame(dataset)
st.session_state.df = validate_and_clean_dataset(st.session_state.df)
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 = pd.read_csv(uploaded_file)
st.session_state.df = validate_and_clean_dataset(st.session_state.df)
st.success("File uploaded successfully!")
except Exception as e:
st.error(f"Error reading uploaded file: {e}")
st.title("Chat with Patent Dataset Using PandasAI")
# Instructions
with st.sidebar:
st.header("Instructions:")
st.markdown(
"1. Select how you want to input the dataset.\n"
"2. Upload, select, or fetch the dataset using the provided options.\n"
"3. Enter a question to interact with the patent data.\n"
" - Example: 'Predict if the patent will be accepted.'\n"
" - Example: 'What is the primary classification of this patent?'\n"
" - Example: 'Summarize the abstract of this patent.'\n"
"4. Enter a query to generate and view graphs based on patent attributes.\n"
)
# Load dataset into session
load_dataset_into_session()
if "df" in st.session_state:
df = st.session_state.df
st.write("### Data Preview")
st.dataframe(df.head(10))
# Create SmartDataFrame
chat_df = SmartDataframe(df, config={"llm": llm})
st.write("### Chat with Your Patent Data")
user_query = st.text_input("Enter your question about the patent data (e.g., 'Predict if the patent will be accepted.'):")
if user_query:
try:
response = chat_df.chat(user_query)
st.success(f"Response: {response}")
except Exception as e:
st.error(f"Error: {e}")
st.write("### Generate and View Graphs")
plot_query = st.text_input("Enter a query to generate a graph (e.g., 'Plot the number of patents by filing year.'):")
if plot_query:
try:
with tempfile.TemporaryDirectory() as temp_dir:
# PandasAI can handle plotting
chat_df.chat(plot_query)
# Save and display the plot
temp_plot_path = os.path.join(temp_dir, "plot.png")
plt.savefig(temp_plot_path)
st.image(temp_plot_path, caption="Generated Plot", use_column_width=True)
except Exception as e:
st.error(f"Error: {e}")
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