Delete interim/incorrect.py

interim/incorrect.py

@@ -1,149 +0,0 @@
-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:
-                # Split query execution into smaller chunks if needed
-                max_rows = 1000
-                total_rows = len(df)
-                results = []
-
-                for start in range(0, total_rows, max_rows):
-                    chunk = df.iloc[start:start + max_rows]
-                    chunk.to_csv("chunk_data.csv", index=False)
-                    partial_agent = create_csv_agent(
-                        ChatOpenAI(temperature=0, model="gpt-4", api_key=os.getenv("OPENAI_API_KEY")),
-                        path=["chunk_data.csv"],
-                        verbose=True,
-                        agent_type=AgentType.OPENAI_FUNCTIONS,
-                        allow_dangerous_code=True
-                    )
-                    result = partial_agent.invoke(query)
-                    results.append(result)
-
-                st.success("Query executed successfully!")
-                st.write("### Query Result:")
-                st.write("\n".join(results))
-
-            except Exception as e:
-                st.error(f"Error executing query: {e}")