Create impressive.py

lab/impressive.py

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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 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}")
+
+    # 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)"
+    )