Update app.py

app.py

@@ -52,10 +52,10 @@ def main():
         if st.button("Run Agent") and query:
             # Initialize LLM with max_retries at the model level
             llm = ChatGoogleGenerativeAI(
-                model="gemini-2.5-flash", 
+                model="gemini-2.5-flash-preview-09-2025", 
                 google_api_key=GEMINI_API_KEY,
                 temperature=0,
-                max_retries=6, # LangChain's built-in retry logic
+                max_retries=6,
             )
 
             # Updated Prefix to strictly separate Thought, Action, and Final Answer
@@ -66,14 +66,21 @@ def main():
             STRICT FORMATTING RULES:
             1. NEVER provide a 'Final Answer' and an 'Action' in the same response.
             2. If you need to run code, provide ONLY the 'Action' and 'Action Input'.
-            3. Once you have the result, provide ONLY the 'Final Answer'.
-            4. Your 'Action Input' must be valid Python code and ONLY Python code. No markdown backticks.
-            5. Before creating a visualization for filtered data, always check if the filtered dataframe is empty.
-            6. For plots, use `plt.figure()` and end with `st.pyplot(plt.gcf())`.
+            3. Your 'Action Input' must be valid Python code and ONLY Python code. No markdown backticks.
+            4. Before creating a visualization for filtered data, always check if the filtered dataframe is empty.
+            5. For plots, use `plt.figure()` and end with `st.pyplot(plt.gcf())`.
+            6. The visuals must be clear and perfect .
+            7. Avoide over writing .
+            """
+
+            # The suffix reinforces the format right before the model generates text
+            custom_suffix = """
+            Begin!
+            Question: {input}
+            Thought: {agent_scratchpad}
             """
 
             # Create the Pandas Agent
-            # Using agent_executor_kwargs to pass handle_parsing_errors to the executor directly
             agent = create_pandas_dataframe_agent(
                 llm,
                 df,
@@ -81,6 +88,7 @@ def main():
                 agent_type="zero-shot-react-description",
                 allow_dangerous_code=True,
                 prefix=custom_prefix,
+                suffix=custom_suffix,
                 handle_parsing_errors=True,
                 agent_executor_kwargs={
                     "handle_parsing_errors": True
@@ -93,7 +101,6 @@ def main():
             st_callback = StreamlitCallbackHandler(thought_container)
 
             with st.spinner("Agent is analyzing..."):
-                # Manual retry logic wrapper for the whole agentic loop
                 max_loop_retries = 3
                 for attempt in range(max_loop_retries):
                     try:
@@ -101,7 +108,7 @@ def main():
                         st.markdown("---")
                         st.subheader("✅ Final Analysis Result")
                         st.success(response)
-                        break # Exit loop if successful
+                        break
                     
                     except Exception as e:
                         if "429" in str(e) or "RESOURCE_EXHAUSTED" in str(e):
@@ -113,7 +120,6 @@ def main():
                             else:
                                 st.error("Rate limit exceeded consistently. Please try again in a minute.")
                         else:
-                            # Enhanced error display for parsing issues
                             st.error(f"Agent encountered a parsing or execution error.")
                             with st.expander("Show Technical Error"):
                                 st.code(str(e))