Add application file

app.py

@@ -0,0 +1,166 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from langchain_community.llms import OpenAI
+from langchain.agents.agent_types import AgentType
+from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
+import textwrap
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from functools import partial
+import time
+
+# Initialize session state
+if 'step' not in st.session_state:
+    st.session_state.step = 1
+if 'dataframes' not in st.session_state:
+    st.session_state.dataframes = {}
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+if 'cleaning_operations' not in st.session_state:
+    st.session_state.cleaning_operations = {}
+
+def main():
+    st.title("Data Analysis Chat App")
+
+    if st.session_state.step == 1:
+        step_1_upload_and_analyze()
+    elif st.session_state.step == 2:
+        step_2_clean_data()
+    elif st.session_state.step == 3:
+        step_3_chat_with_data()
+
+def step_1_upload_and_analyze():
+    st.subheader("Step 1: Upload and Analyze Data")
+
+    uploaded_files = st.file_uploader("Upload CSV files", type="csv", accept_multiple_files=True)
+    if uploaded_files:
+        for file in uploaded_files:
+            df = pd.read_csv(file)
+            st.session_state.dataframes[file.name] = df
+            st.success(f"Uploaded: {file.name}")
+
+        if st.button("Analyze Data"):
+            for name, df in st.session_state.dataframes.items():
+                st.write(f"Analysis for {name}:")
+                st.write(f"Shape: {df.shape}")
+                st.write("Columns:")
+                st.write(df.columns.tolist())
+                st.write("Preview:")
+                st.write(df.head())
+                st.write("---")
+
+        if st.button("Proceed to Data Cleaning"):
+            st.session_state.step = 2
+
+def step_2_clean_data():
+    st.subheader("Step 2: Clean Data")
+
+    llm = OpenAI(temperature=0)
+    
+    for name, df in st.session_state.dataframes.items():
+        st.write(f"Cleaning recommendations for {name}:")
+        
+        # Create a summary of the dataframe
+        summary = f"Dataframe '{name}' summary:\n"
+        summary += f"- Shape: {df.shape}\n"
+        summary += f"- Columns: {', '.join(df.columns)}\n"
+        summary += "- Data types:\n"
+        for col, dtype in df.dtypes.items():
+            summary += f"  - {col}: {dtype}\n"
+        summary += "- Sample data (first 5 rows):\n"
+        summary += df.head().to_string()
+
+        # Split the summary into smaller chunks
+        chunk_size = 1500  # Reduced chunk size
+        chunks = textwrap.wrap(summary, chunk_size)
+
+        cleaning_recommendations = []
+        with st.spinner("Analyzing data and generating recommendations..."):
+            for i, chunk in enumerate(chunks):
+                chunk_result = analyze_chunk(llm, df, chunk)
+                cleaning_recommendations.append(chunk_result)
+
+        # Combine all recommendations
+        full_recommendations = "\n".join(cleaning_recommendations)
+        st.write(full_recommendations)
+        
+        # Create checkboxes for cleaning operations
+        cleaning_ops = [op.strip() for op in full_recommendations.split('\n') if op.strip()]
+        st.session_state.cleaning_operations[name] = []
+        for op in cleaning_ops:
+            if st.checkbox(op, key=f"{name}_{op}"):
+                st.session_state.cleaning_operations[name].append(op)
+
+    if st.button("Apply Cleaning and Proceed to Chat"):
+        for name, ops in st.session_state.cleaning_operations.items():
+            df = st.session_state.dataframes[name]
+            for op in ops:
+                # Here you would implement the actual cleaning operations
+                # For now, we'll just print what would be done
+                st.write(f"Applying to {name}: {op}")
+        
+        st.session_state.step = 3
+        st.success("Cleaning operations applied. Proceeding to chat interface.")
+        st.button("Go to Chat Interface")
+
+    if st.button("Back to Data Upload"):
+        st.session_state.step = 1
+        st.experimental_rerun()
+
+def step_3_chat_with_data():
+    st.subheader("Step 3: Chat with your data")
+
+    user_input = st.text_input("Ask a question about your data:")
+    if user_input:
+        response = process_user_input(user_input)
+        st.session_state.chat_history.append(("User", user_input))
+        st.session_state.chat_history.append(("AI", response))
+
+    for role, message in st.session_state.chat_history:
+        if role == "User":
+            st.text_area("You:", value=message, height=50, disabled=True)
+        else:
+            st.text_area("AI:", value=message, height=100, disabled=True)
+
+def process_user_input(user_input):
+    llm = OpenAI(temperature=0)
+    combined_df = pd.concat([df.assign(source=name) for name, df in st.session_state.dataframes.items()], ignore_index=True)
+
+    df_summary = "Available data:\n"
+    for name, df in st.session_state.dataframes.items():
+        df_summary += f"- {name}: {len(df)} rows, {len(df.columns)} columns\n"
+        df_summary += f"  Columns: {', '.join(df.columns)}\n\n"
+
+    agent = create_pandas_dataframe_agent(
+        llm,
+        combined_df,
+        verbose=True,
+        agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        allow_dangerous_code=True
+    )
+
+    full_input = f"{df_summary}\nThe data from all files has been combined into a single DataFrame with an additional 'source' column indicating the original file.\n\nUser question: {user_input}"
+
+    response = agent.run(full_input)
+    return response
+
+def analyze_chunk(llm, df, chunk, timeout=30):
+    agent = create_pandas_dataframe_agent(
+        llm,
+        df,
+        verbose=True,
+        agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        allow_dangerous_code=True
+    )
+    
+    prompt = f"Analyze this part of the dataframe summary and suggest up to 3 specific cleaning operations. Focus on identifying missing values, outliers, and inconsistent data formats.\n\n{chunk}"
+    
+    try:
+        with ThreadPoolExecutor() as executor:
+            future = executor.submit(agent.run, prompt)
+            return future.result(timeout=timeout)
+    except Exception as e:
+        return f"Analysis timed out or encountered an error: {str(e)}"
+
+if __name__ == "__main__":
+    main()