Update app.py

app.py

@@ -10,6 +10,9 @@ import io
 import base64
 import requests
 
+
+
+
 # API Endpoint and payload
 API_URL = "https://irisplus.elixir.co.zw/public/api/profile/reporting/stock-card/genericReports"
 PAYLOAD = {
@@ -37,6 +40,21 @@ def fetch_data():
         st.error(f"Error fetching data: {response.status_code} - {response.text}")
         return None
 
+gemini_api_key = os.environ['GOOGLE_API_KEY']
+
+genai.configure(api_key=gemini_api_key)
+
+generation_config = {
+  "temperature": 0.2,
+  "top_p": 0.95,
+  "max_output_tokens": 5000,
+}
+
+model = genai.GenerativeModel(
+  model_name="gemini-2.0-flash-thinking-exp",
+  generation_config=generation_config,
+)
+
 class StreamLitResponse(ResponseParser):
     def __init__(self, context):
         super().__init__(context)
@@ -83,6 +101,21 @@ if not GOOGLE_API_KEY:
     st.error("GOOGLE_API_KEY environment variable not set.")
     st.stop()
 
+gemini_api_key = os.environ['GOOGLE_API_KEY']
+
+genai.configure(api_key=gemini_api_key)
+
+generation_config = {
+  "temperature": 0.2,
+  "top_p": 0.95,
+  "max_output_tokens": 5000,
+}
+
+model = genai.GenerativeModel(
+  model_name="gemini-2.0-flash-thinking-exp",
+  generation_config=generation_config,
+)
+
 def generateResponse(prompt, df):
     """Generate response using PandasAI with SmartDataframe"""
     llm = GoogleGemini(api_key=GOOGLE_API_KEY)
@@ -206,13 +239,13 @@ def main():
                 with st.expander("Preview"):
                     st.dataframe(filtered_df.head())
                 with st.spinner("Generating Report, Please Wait...."):
-                    prompt = (
+                    prompt = f"""
                         "You are an expert business analyst. Analyze the following data and generate a comprehensive and insightful business report "
-                        "including key performance indicators and recommendations.\n\nData:\n" + str(filtered_df.to_json(orient='records'))
-                    )
-                    response = generateResponse(prompt, filtered_df)
+                        "including key performance indicators and recommendations.\n\nData:\n" + {str(filtered_df.to_json(orient='records'))}
+                    """
+                    response = model.generate_content(prompt)
                     # Display the report text; adjust according to your response format.
-                    st.markdown(response.get("value", "No report generated."))
+                    st.markdown(response.txt)
         else:
             st.error("No data available for reports.")