Update app.py

app.py

@@ -5,6 +5,7 @@ import numpy as np
 import requests
 import json
 import time # Ensure time is imported for backoff
+import matplotlib.pyplot as plt # Explicitly import matplotlib.pyplot here for use in exec() scope
 
 # --- CONFIG ---
 # Note: GEMINI_API_KEY is retrieved from environment variables/secrets.
@@ -41,7 +42,7 @@ SYSTEM_INSTRUCTION = (
     "2. **Code:** If the question requires calculation, aggregation, or visualization, you MUST generate Python code to execute against the 'df' DataFrame. "
     "   - The DataFrame is already loaded as a variable named 'df'. Do NOT redefine it. "
     "   - Use Streamlit functions for output: `st.dataframe(...)` for results, `st.bar_chart()`, `st.line_chart()`, or `st.pyplot()` for plots. "
-    "   - Use `import matplotlib.pyplot as plt` if creating custom plots. "
+    "   - If you need custom plotting, you can assume `import matplotlib.pyplot as plt` is available and use `plt.figure()`, `plt.bar()`, etc., followed by `st.pyplot(plt)` to display the plot. "
     "   - Ensure the code is self-contained and ready to execute."
 )
 
@@ -91,7 +92,7 @@ def chat_with_gemini(prompt, context):
             raise e
 
 # --- UI ---
-st.title("✨ Perfect Data Analyst Agent (Code Execution Enabled)")
+st.title("✨Data Analyst Agent (Code Execution Enabled)")
 st.write("Upload a CSV file and ask natural language questions. The agent now generates and executes Python code to provide precise data analysis and visualizations.")
 
 # State variable to hold the DataFrame, initialized once
@@ -122,7 +123,8 @@ if uploaded:
             df = st.session_state.df # Local variable for code execution context
             
             # Summarize dataset for context sent to the LLM
-            context = f"Dataset Columns: {', '.join(df.columns.astype(str))}\n\nFirst 5 rows of data:\n{df.head(5).to_markdown(index=False)}"
+            # FIX: Using to_string() instead of to_markdown() to avoid the 'tabulate' dependency error.
+            context = f"Dataset Columns: {', '.join(df.columns.astype(str))}\n\nFirst 5 rows of data:\n{df.head(5).to_string(index=False)}"
 
             st.markdown("---")
             st.subheader("🤖 Analysis Steps")
@@ -151,12 +153,13 @@ if uploaded:
                     try:
                         # 2. Execute the generated Python code safely
                         
-                        # IMPORTANT: Create a local scope with necessary variables (df, st)
+                        # IMPORTANT: Create a local scope with necessary variables
                         local_scope = {
                             'df': df, 
                             'st': st, 
                             'pd': pd,
                             'np': np,
+                            'plt': plt, # Explicitly include pyplot here
                         }
                         # Executing the code within the local scope
                         exec(code, globals(), local_scope)
@@ -164,5 +167,12 @@ if uploaded:
                         st.success("Code execution complete. Results are displayed above.")
 
                     except Exception as e:
-                        st.error(f"Step 2 Failed (Code Execution Error): The agent generated invalid code.")
-                        st.exception(e)
+                        st.error(f"Step 2 Failed (Code Execution Error): The agent generated invalid code. Check the console for full traceback.")
+                        st.exception(e)
+            else:
+                st.info("No code was generated, as the question was purely informational.")
+    else:
+        st.info("The uploaded CSV file appears to be empty.")
+
+else:
+    st.info("👆 Upload a CSV file to begin the full analysis experience.")