Update app.py

app.py

@@ -10,6 +10,7 @@ import io
 import base64
 import requests
 import google.generativeai as genai
+from fpdf import FPDF  # New import for PDF generation
 
 # API Endpoint and payload
 API_URL = "https://irisplus.elixir.co.zw/public/api/profile/reporting/stock-card/genericReports"
@@ -38,7 +39,11 @@ def fetch_data():
         st.error(f"Error fetching data: {response.status_code} - {response.text}")
         return None
 
-gemini_api_key = os.environ['GOOGLE_API_KEY']
+# Configure Gemini API
+gemini_api_key = os.environ.get('GOOGLE_API_KEY')
+if not gemini_api_key:
+    st.error("GOOGLE_API_KEY environment variable not set.")
+    st.stop()
 
 genai.configure(api_key=gemini_api_key)
 
@@ -94,29 +99,9 @@ class StreamLitResponse(ResponseParser):
             'value': str(result['value'])
         }
 
-GOOGLE_API_KEY = os.environ.get('GOOGLE_API_KEY')
-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)
+    llm = GoogleGemini(api_key=gemini_api_key)
     pandas_agent = SmartDataframe(df, config={
         "llm": llm, 
         "response_parser": StreamLitResponse
@@ -188,6 +173,21 @@ def handle_userinput(question, df):
     except Exception as e:
         st.error(f"Error processing input: {e}")
 
+def generate_pdf(report_text):
+    """
+    Generate a PDF in memory from the report text.
+    This function uses FPDF to create a PDF and returns its bytes.
+    """
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("Arial", size=12)
+    # Split report text into lines for writing
+    for line in report_text.split('\n'):
+        pdf.multi_cell(0, 10, line)
+    # Output PDF to a byte string
+    pdf_bytes = pdf.output(dest="S").encode("latin1")
+    return pdf_bytes
+
 def main():
     st.set_page_config(page_title="AI Chat with Your Data", page_icon="📊")
 
@@ -238,12 +238,25 @@ def main():
                     st.dataframe(filtered_df.head())
                 with st.spinner("Generating Report, Please Wait...."):
                     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'))}
+                        "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 = model.generate_content(prompt)
                     # Display the report text; adjust according to your response format.
                     report = response.text
+                    
+                    # Generate the PDF bytes from the report text
+                    pdf_bytes = generate_pdf(report)
+                    
+                    # Display the download button above the report
+                    st.download_button(
+                        label="Download Report as PDF",
+                        data=pdf_bytes,
+                        file_name="report.pdf",
+                        mime="application/pdf"
+                    )
+                    
+                    # Display the report content
                     st.markdown(report)
         else:
             st.error("No data available for reports.")