Update app3.py

app3.py

@@ -2,15 +2,21 @@ import streamlit as st
 import pandas as pd
 import plotly.express as px
 from pandasai import Agent
+from pandasai.llm.openai import OpenAI
 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
 import os
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
 
 # Set the title of the app
-st.title("Data Analyzer")
+st.title("Data Analyzer on Hugging Face Spaces")
 
 # Fetch API keys from environment variables
 api_key = os.getenv("OPENAI_API_KEY")
@@ -20,85 +26,109 @@ if not api_key or not pandasai_api_key:
     st.error(
         "API keys not found in the environment. Please set the 'OPENAI_API_KEY' and 'PANDASAI_API_KEY' environment variables."
     )
+    logger.error("API keys not found. Ensure they are set in the environment variables.")
 else:
     # File uploader
     uploaded_file = st.file_uploader("Upload an Excel or CSV file", type=["xlsx", "csv"])
 
     if uploaded_file is not None:
-        # Load the data
-        if uploaded_file.name.endswith('.xlsx'):
-            df = pd.read_excel(uploaded_file)
-        else:
-            df = pd.read_csv(uploaded_file)
-
-        st.write("Data Preview:")
-        st.write(df.head())
-
-        # Set up PandasAI Agent
-        agent = Agent(df)
-
-        # Convert the DataFrame into documents
-        documents = [
-            Document(
-                page_content=", ".join([f"{col}: {row[col]}" for col in df.columns]),
-                metadata={"index": index}
+        try:
+            # Load the data
+            if uploaded_file.name.endswith('.xlsx'):
+                df = pd.read_excel(uploaded_file)
+            else:
+                df = pd.read_csv(uploaded_file)
+
+            st.write("Data Preview:")
+            st.write(df.head())
+            logger.info(f"Uploaded file loaded successfully with shape: {df.shape}")
+
+            # Initialize PandasAI Agent
+            llm = OpenAI(api_key=pandasai_api_key, max_tokens=1500, timeout=60)
+            agent = Agent(df, llm=llm)
+
+            # Convert the DataFrame into documents for RAG
+            documents = [
+                Document(
+                    page_content=", ".join([f"{col}: {row[col]}" for col in df.columns if pd.notnull(row[col])]),
+                    metadata={"index": index}
+                )
+                for index, row in df.iterrows()
+            ]
+            logger.info(f"{len(documents)} documents created for RAG.")
+
+            # 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
             )
-            for index, row in 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
-        tab1, tab2, tab3 = st.tabs(["PandasAI Analysis", "RAG Q&A", "Data Visualization"])
-
-        with tab1:
-            st.header("Data Analysis using PandasAI")
-            pandas_question = st.text_input("Ask a question about the data (PandasAI):")
-            if pandas_question:
-                result = agent.chat(pandas_question)
-                st.write("PandasAI Answer:", result)
-
-        with tab2:
-            st.header("Question Answering using RAG")
-            rag_question = st.text_input("Ask a question about the 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 create? (e.g., 'Show a scatter plot of salary vs experience')")
-            
-            if viz_question:
-                try:
-                    result = agent.chat(viz_question)
-                    
-                    # Since PandasAI output is text, extract 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 code to use Plotly (px) instead of matplotlib (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 chart
-                        exec(viz_code)
-                        st.plotly_chart(fig)
-                    else:
-                        st.write("Unable to generate a graph. Please try a different query.")
-                except Exception as e:
-                    st.write(f"An error occurred: {str(e)}")
-                    st.write("Please try phrasing your query differently.")
+
+            # Create tabs
+            tab1, tab2, tab3 = st.tabs(["PandasAI Analysis", "RAG Q&A", "Data Visualization"])
+
+            # Tab 1: PandasAI Analysis
+            with tab1:
+                st.header("Data Analysis using PandasAI")
+                pandas_question = st.text_input("Ask a question about the data (PandasAI):")
+                if pandas_question:
+                    try:
+                        result = agent.chat(pandas_question)
+                        if result:
+                            st.write("PandasAI Answer:", result)
+                        else:
+                            st.warning("PandasAI returned no result. Please try another question.")
+                    except Exception as e:
+                        st.error(f"Error from PandasAI: {e}")
+                        logger.error(f"PandasAI error: {e}")
+
+            # Tab 2: RAG Q&A
+            with tab2:
+                st.header("Question Answering using RAG")
+                rag_question = st.text_input("Ask a question about the data (RAG):")
+                if rag_question:
+                    try:
+                        result = qa_chain.run(rag_question)
+                        st.write("RAG Answer:", result)
+                    except Exception as e:
+                        st.error(f"Error from RAG Q&A: {e}")
+                        logger.error(f"RAG error: {e}")
+
+            # Tab 3: Data Visualization
+            with tab3:
+                st.header("Data Visualization")
+                viz_question = st.text_input("What kind of graph would you like to create? (e.g., 'Show a scatter plot of salary vs experience')")
+                if viz_question:
+                    try:
+                        result = agent.chat(viz_question)
+
+                        # Since PandasAI output is text, extract 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)
+                            logger.debug(f"Extracted visualization code: {viz_code}")
+
+                            # Modify code to use Plotly (px) instead of matplotlib (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 chart
+                            exec(viz_code)
+                            st.plotly_chart(fig)
+                        else:
+                            st.warning("Unable to generate a graph. Please try a different query.")
+                            logger.warning("No valid visualization code found in PandasAI response.")
+                    except Exception as e:
+                        st.error(f"An error occurred: {e}")
+                        logger.error(f"Visualization error: {e}")
+        except Exception as e:
+            st.error(f"An error occurred while processing the file: {e}")
+            logger.error(f"File processing error: {e}")
     else:
         st.info("Please upload a file to begin analysis.")