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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 | |
| from langchain_groq import ChatGroq | |
| from langchain_openai import ChatOpenAI | |
| import time | |
| # Load environment variables | |
| openai_api_key = os.getenv("OPENAI_API_KEY") | |
| groq_api_key = os.getenv("GROQ_API_KEY") | |
| st.title("Chat with Patent Dataset Using PandasAI") | |
| # Initialize the LLM based on user selection | |
| def initialize_llm(model_choice): | |
| 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") | |
| # Select LLM model | |
| model_choice = st.radio("Select LLM", ["GPT-4o", "llama-3.3-70b"], index=0, horizontal=True) | |
| llm = initialize_llm(model_choice) | |
| # 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 and llm: | |
| 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)) | |
| # Create SmartDataFrame | |
| chat_df = SmartDataframe(df, config={"llm": llm}) | |
| # Chat functionality | |
| st.write("### Chat with 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}") | |
| # Plot generation functionality | |
| 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_container_width=True) | |
| except Exception as e: | |
| st.error(f"Error: {e}") | |
| # Download processed dataset | |
| #st.write("### Download Processed Dataset") | |
| #st.download_button( | |
| # label="Download Dataset as CSV", | |
| # data=df.to_csv(index=False), | |
| # file_name="processed_dataset.csv", | |
| # mime="text/csv" | |
| #) | |
| # Sidebar instructions | |
| with st.sidebar: | |
| st.header("📋 Instructions:") | |
| st.markdown( | |
| "1. Choose an LLM (Groq-based or OpenAI-based) to interact with the data.\n" | |
| "2. Upload, select, or fetch the dataset using the provided options.\n" | |
| "3. Enter a query to generate and view graphs based on patent attributes.\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" | |
| ) | |
| st.markdown("---") | |
| st.header("📚 References:") | |
| st.markdown( | |
| "1. [Chat With Your CSV File With PandasAI - Prince Krampah](https://medium.com/aimonks/chat-with-your-csv-file-with-pandasai-22232a13c7b7)" | |
| ) | |