Update app.py

app.py

@@ -3,67 +3,72 @@ import pandas as pd
 from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
 from langchain_google_genai import ChatGoogleGenerativeAI
 import os
+import traceback
 
 def main():
     st.set_page_config(page_title="Data Analysis Agent 🤖")
     st.title("🤖 Data Analysis Agent")
     st.write("Upload a CSV file and ask questions about your data.")
 
-    # Get Gemini API Key from Streamlit Secrets
-    # We use st.secrets for deployment on Hugging Face
+    # --- API Key Setup ---
+    # This block tries to get the API key from Hugging Face secrets
+    api_key = None
     try:
-        # For local development, you can set an environment variable
-        # For deployment, set this in Hugging Face Spaces "Secrets"
-        api_key = os.environ.get("GEMINI_API_KEY") or st.secrets["GEMINI_API_KEY"]
+        api_key = st.secrets["GEMINI_API_KEY"]
     except KeyError:
-        st.error("GEMINI_API_KEY not found. Please set it in your Hugging Face Spaces secrets.")
-        return
+        st.error("🔴 GEMINI_API_KEY not found in Hugging Face secrets!")
+        st.write("Please go to your Space's **Settings > Repository secrets** and add your Google AI Studio API key.")
+        st.write("The **Name** must be `GEMINI_API_KEY`.")
+        return # Stop the app if key is missing
 
     if not api_key:
         st.warning("Please add your Gemini API Key to the Hugging Face Space Secrets to use the app.")
         return
 
-    # File Uploader
+    # --- File Uploader ---
     uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
 
     if uploaded_file is not None:
         try:
-            # Read the CSV file into a Pandas DataFrame
             df = pd.read_csv(uploaded_file)
             st.dataframe(df.head())
 
             # Initialize the Gemini LLM
             llm = ChatGoogleGenerativeAI(
-                model="gemini-pro",
+                model="gemini-1.5-pro-latest", # Using the latest, most capable model
                 google_api_key=api_key,
                 temperature=0
             )
 
             # Create the Pandas DataFrame Agent
-            # allow_dangerous_code=True is required for the agent to execute Python code
             agent = create_pandas_dataframe_agent(
                 llm,
                 df,
-                verbose=True,
-                allow_dangerous_code=True 
+                verbose=True, # This will print the agent's thoughts in the log
+                allow_dangerous_code=True,
+                handle_parsing_errors=True # Helps with bad LLM outputs
             )
 
-            # User question input
+            # --- User Interaction ---
             user_question = st.text_input("Ask a question about your data:")
 
             if user_question:
                 with st.spinner("Analyzing..."):
                     try:
-                        # The agent.invoke method runs the agent and returns a dictionary
-                        # The actual answer is in the "output" key
+                        # Invoke the agent
                         response = agent.invoke(user_question)
                         st.write("### Answer")
                         st.success(response["output"])
+                    
                     except Exception as e:
-                        st.error(f"Error analyzing data: {e}")
+                        # This is the most important error block
+                        st.error(f"🔴 An error occurred while analyzing the data.")
+                        st.write("Here is the full error message:")
+                        # Print the full error stack trace to the app
+                        st.exception(e)
 
         except Exception as e:
-            st.error(f"Error reading file: {e}")
+            st.error(f"🔴 An error occurred while reading the file: {e}")
 
 if __name__ == "__main__":
     main()