Update app.py

app.py

@@ -5,6 +5,8 @@ import pandas as pd
 from pandasai import SmartDataframe
 from pandasai.responses.response_parser import  ResponseParser
 from st_on_hover_tabs import on_hover_tabs
+from ydata_profiling import ProfileReport
+import google.generativeai as genai
 
 class StreamLitResponse(ResponseParser):
         def __init__(self,context) -> None:
@@ -21,6 +23,19 @@ class StreamLitResponse(ResponseParser):
 
 gemini_api_key = os.environ['Gemini']
 
+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-1.5-flash",
+  generation_config=generation_config,
+)
+
 def calculate_kpis(df):
     """
     Calculates key performance indicators from a given transaction dataset.
@@ -69,6 +84,12 @@ def calculate_kpis(df):
 
     return json.dumps(kpis, indent=4)
 
+
+def get_pandas_profile(df):
+    profile = ProfileReport(df, title="Profiling Report")
+    json_profile = profile.to_json()
+    return json_profile
+    
 def generateResponse(dataFrame,prompt):
         llm = GoogleGemini(api_key=gemini_api_key)
         pandas_agent = SmartDataframe(dataFrame,config={"llm":llm, "response_parser":StreamLitResponse})
@@ -105,4 +126,13 @@ if tabs =='Chat':
         st.write(answer)
 
 elif tabs == 'Reports':
-        st.header("Reports")
+    st.header("Reports")
+    prompt = """
+You are an expert business analyst. Analyze the following data and generate a comprehensive and insightful business report, including appropriate key perfomance indicators and reccomendations.
+
+data:
+""" + str(calculate_kpis(df)) + str(get_pandas_profile(df))
+
+    response = model.generate_content(prompt)
+    report = response.text
+    st.write(report)