Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from pandasai import Agent
+from langchain_community.embeddings.openai import OpenAIEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_openai import ChatOpenAI
+from langchain.chains import RetrievalQA
+from langchain.schema import Document
+from datasets import load_dataset  
+import os
+
+# Title
+st.title("PandasAI Data Analysis Tool with RAG")
+
+# Fetch API keys from environment variables
+api_key = os.getenv("OPENAI_API_KEY")
+pandasai_api_key = os.getenv("PANDASAI_API_KEY")
+
+# Dataset selection
+st.sidebar.header("Dataset Input Options")
+input_option = st.sidebar.radio("Select Dataset Input:", ["Use Hugging Face Dataset", "Upload CSV File"])
+
+# Initialize session state for the dataframe
+if "df" not in st.session_state:
+    st.session_state.df = None
+
+# Dataset loading logic
+if input_option == "Use Hugging Face Dataset":
+    dataset_name = st.sidebar.text_input("Enter Hugging Face Dataset Name:", value="HUPD/hupd")
+    if st.sidebar.button("Load Dataset"):
+        try:
+            dataset = load_dataset(dataset_name, name="sample", split="train", trust_remote_code=True)
+            st.session_state.df = pd.DataFrame(dataset)
+            st.sidebar.success(f"Dataset '{dataset_name}' loaded successfully!")
+        except Exception as e:
+            st.sidebar.error(f"Error loading dataset: {e}")
+elif input_option == "Upload CSV File":
+    uploaded_file = st.sidebar.file_uploader("Upload CSV File:", type=["csv"])
+    if uploaded_file:
+        try:
+            st.session_state.df = pd.read_csv(uploaded_file)
+            st.sidebar.success("File uploaded successfully!")
+        except Exception as e:
+            st.sidebar.error(f"Error loading file: {e}")
+
+# Show the loaded dataframe preview
+if st.session_state.df is not None:
+    st.subheader("Dataset Preview")
+    st.dataframe(st.session_state.df.head(10))
+
+    # Set up PandasAI Agent
+    agent = Agent(st.session_state.df)
+
+    # Convert DataFrame to documents
+    documents = [
+        Document(
+            page_content=", ".join([f"{col}: {row[col]}" for col in st.session_state.df.columns]),
+            metadata={"index": index}
+        )
+        for index, row in st.session_state.df.iterrows()
+    ]
+
+    # Set up RAG
+    embeddings = OpenAIEmbeddings()
+    vectorstore = FAISS.from_documents(documents, embeddings)
+    retriever = vectorstore.as_retriever()
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=ChatOpenAI(),
+        chain_type="stuff",
+        retriever=retriever
+    )
+
+    # Create tabs for different functionality
+    tab1, tab2, tab3 = st.tabs(["PandasAI Analysis", "RAG Q&A", "Data Visualization"])
+
+    with tab1:
+        st.header("Data Analysis with PandasAI")
+        pandas_question = st.text_input("Ask a question about your data (PandasAI):")
+        if pandas_question:
+            result = agent.chat(pandas_question)
+            st.write("PandasAI Answer:", result)
+
+    with tab2:
+        st.header("Q&A with RAG")
+        rag_question = st.text_input("Ask a question about your data (RAG):")
+        if rag_question:
+            result = qa_chain.run(rag_question)
+            st.write("RAG Answer:", result)
+
+    with tab3:
+        st.header("Data Visualization")
+        viz_question = st.text_input("What kind of graph would you like to see? (e.g., 'Show a scatter plot of salary vs experience')")
+        if viz_question:
+            try:
+                result = agent.chat(viz_question)
+                
+                # Convert the PandasAI result into executable code
+                import re
+                code_pattern = r'```python\n(.*?)\n```'
+                code_match = re.search(code_pattern, result, re.DOTALL)
+                
+                if code_match:
+                    viz_code = code_match.group(1)
+                    # Modify the code to use 'px' instead of 'plt'
+                    viz_code = viz_code.replace('plt.', 'px.')
+                    viz_code = viz_code.replace('plt.show()', 'fig = px.scatter(df, x=x, y=y)')
+                    
+                    # Execute the code and display the graph
+                    exec(viz_code)
+                    st.plotly_chart(fig)
+                else:
+                    st.write("Failed to generate a graph. Please try asking differently.")
+            except Exception as e:
+                st.write(f"An error occurred: {str(e)}")
+                st.write("Please try rephrasing your question.")
+else:
+    st.warning("No dataset loaded. Please select a dataset input option from the sidebar.")
+
+# Error handling for missing API keys
+if not api_key:
+    st.error("Missing OpenAI API Key in environment variables.")
+if not pandasai_api_key:
+    st.error("Missing PandasAI API Key in environment variables.")