Create incorrect.py

interim/incorrect.py

@@ -0,0 +1,149 @@
+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}")