Update app.py

app.py

@@ -3,24 +3,39 @@ import gradio as gr
 from transformers import pipeline
 from langchain_community.llms import HuggingFacePipeline
 from langchain_experimental.agents import create_pandas_dataframe_agent
+from langchain.agents.agent_types import AgentType
 
 # Load data
 df = pd.read_csv("synthetic_profit.csv")
 
-# Set up lightweight Hugging Face pipeline (Flan-T5 Base)
+# Lightweight Hugging Face pipeline (Flan-T5-base)
 hf_pipeline = pipeline(
     task="text2text-generation",
     model="google/flan-t5-base",
     device=-1  # CPU
 )
 
-# LangChain agent setup (with explicit security opt-in)
+# LangChain LLM
 llm = HuggingFacePipeline(pipeline=hf_pipeline)
-agent = create_pandas_dataframe_agent(llm, df, verbose=True, allow_dangerous_code=True)
 
+# Create LangChain agent with explicit parsing-error handling
+agent = create_pandas_dataframe_agent(
+    llm, 
+    df, 
+    verbose=True, 
+    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+    handle_parsing_errors=True,
+    allow_dangerous_code=True
+)
+
+# Answer query function with clearer prompts
 def answer(query: str) -> str:
     try:
-        response = agent.run(query)
+        prompt = (
+            f"Answer this clearly and numerically without scientific notation: {query}. "
+            "If multiple numbers, provide their total sum clearly."
+        )
+        response = agent.run(prompt)
         return f"📊 {response}"
     except Exception as e:
         return f"❗ Error: {str(e)}"
@@ -28,10 +43,13 @@ def answer(query: str) -> str:
 # Gradio interface
 demo = gr.Interface(
     fn=answer,
-    inputs=gr.Textbox(lines=2, placeholder="E.g., 'Total revenue for Product B in EMEA', 'List products with negative profit.'"),
+    inputs=gr.Textbox(
+        lines=2, 
+        placeholder="E.g., 'Total revenue for Product B in EMEA during Q2 2024'"
+    ),
     outputs="text",
     title="🟢 SAP Profitability Data Chat (Flan-T5 + Pandas)",
-    description="Ask questions about synthetic SAP profitability data. Powered by Flan-T5-base via Hugging Face."
+    description="Ask clearly numeric questions about synthetic SAP profitability data. Results are precise and human-readable."
 )
 
 demo.launch()