Display money needs pattern

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import os
 import google.generativeai as genai
-GOOGLE_API_KEY=os.getenv('GEMINI_API_KEY')
+GOOGLE_API_KEY= 'AIzaSyC0T2fN5Dga-6nkPc6HYV7-bDZskqgALX0' #os.getenv('GEMINI_API_KEY')
 genai.configure(api_key=GOOGLE_API_KEY)
 model = genai.GenerativeModel(model_name = "gemini-pro")
 from utils import findTop3MoneyNeeds
@@ -88,8 +88,14 @@ if submit_button:
     st.write("Entered proposal:", proposal)
     st.write("Analyzing your proposition")
     
-    topMoneyNeeds = findTop3MoneyNeeds(moneyNeeds)
+    topMoneyNeeds, topMoneyNeedsDict = findTop3MoneyNeeds(moneyNeeds)
 
     response = model.generate_content([pre_prompt.format(proposal)])
-    st.write(response.text)
+    st.write("As per your money needs your product is mostly targeting the below spending patterns",)
+    
+    for idx, need in enumerate(topMoneyNeeds):
+        st.write("{}. {}".format(idx+1, need))
+    
+    #st.write(response.text)
 
+    

db.py

@@ -15,12 +15,15 @@ def fetch_db_rows_as_dicts(db_path, table_name):
         # Print the column names
         print("Column names:", column_names)
         
+        
         # Execute a query to fetch all rows from the table
         cursor.execute(f"SELECT * FROM {table_name};")
         rows = cursor.fetchall()
         
+        #for row in rows:
+        #    row_dict = dict(zip(column_names, row))
+        #    print(row_dict)
         assert len(rows) > 1
-
         return column_names, rows[1:]
             
     except sqlite3.Error as e:
@@ -33,4 +36,4 @@ def fetch_db_rows_as_dicts(db_path, table_name):
 
 
 # Example usage:
-# fetch_db_rows_as_dicts('money_needs.sqlite', 'money_needs')
+fetch_db_rows_as_dicts('topologies.sqlite', 'topologies')

utils.py

@@ -1,8 +1,9 @@
 from db import fetch_db_rows_as_dicts
 import google.generativeai as genai
 import json
+import os
 
-GOOGLE_API_KEY='AIzaSyC0T2fN5Dga-6nkPc6HYV7-bDZskqgALX0'
+GOOGLE_API_KEY=os.getenv('GEMINI_API_KEY')
 genai.configure(api_key=GOOGLE_API_KEY)
 model = genai.GenerativeModel(model_name = "gemini-pro")
 
@@ -27,6 +28,33 @@ def transform_to_dict_of_dicts(columns, rows):
 
     # Iterate over each row
     for row in rows:
+        print(dict(row))
+        # The first element of the row is the key for the outer dictionary
+        outer_key = row[0].strip()
+        
+        # Initialize the inner dictionary
+        inner_dict = {}
+        
+        # Iterate over the rest of the elements in the row
+        for i, value in enumerate(row[1:], start=1):
+            # The corresponding column name is the key for the inner dictionary
+            inner_key = columns[i].strip()
+            # Add the key-value pair to the inner dictionary
+            inner_dict[inner_key] = value
+        
+        # Add the inner dictionary to the result dictionary with the outer key
+        result[outer_key] = inner_dict
+
+    return result
+
+
+def transform_topologies_to_dict(columns, rows):
+    # Initialize the result dictionary
+    result = {}
+
+    # Iterate over each row
+    for row in rows:
+        print(dict(row))
         # The first element of the row is the key for the outer dictionary
         outer_key = row[0].strip()
         
@@ -55,7 +83,54 @@ def findTop3MoneyNeeds(proposition):
         needDictIndexes.append(moneyNeedsDict[need])
 
     print(needDictIndexes)
-    return needDictIndexes
+    return needs, needDictIndexes
+
+
+def findTop3Needs(proposition, moneyNeeds):
+    
+    moneyNeedsString = concatenate_keys(moneyNeeds)
+
+    prompt = '''You have these listed needs of customers
+    {}
+
+    Now given a proposition 
+    "{}" 
+    
+    Find the best 3 strings out of the list which matches this proposition. Return output strictly only in json under a list called matches
+    '''
+
+    moneyNeedsPrompt = prompt.format(moneyNeedsString, proposition)
+    response = model.generate_content([moneyNeedsPrompt])
+    output = response.text
+    output = output.replace('```json', '')
+    output = output.replace('```', '')
+    obj = load_json_from_string(output)
+    print(obj)
+    return obj['matches']
+
+
+def findTop3Topologies(proposition):
+    topologies, rows = fetch_db_rows_as_dicts('topologies.sqlite', 'topologies')
+    
+    topologiesDict = transform_to_dict_of_dicts(topologies, rows)
+    print(topologiesDict)
+    # prompt = '''You have these listed needs of customers
+    # {}
+
+    # Now given a proposition 
+    # "{}" 
+    
+    # Find the best 3 strings out of the list which matches this proposition. Return output strictly only in json under a list called matches
+    # '''
+
+    # moneyNeedsPrompt = prompt.format(moneyNeedsString, proposition)
+    # response = model.generate_content([moneyNeedsPrompt])
+    # output = response.text
+    # output = output.replace('```json', '')
+    # output = output.replace('```', '')
+    # obj = load_json_from_string(output)
+    # print(obj)
+    # return obj['matches']
 
 
 def findTop3Needs(proposition, moneyNeeds):
@@ -81,4 +156,7 @@ def findTop3Needs(proposition, moneyNeeds):
     return obj['matches']
 
 
+#findTop3Topologies('We have a product for family people giving them discounts and low interest loans for home appliances. They can pay us back in small instalments over the course of 4 years')
 #findTop3MoneyNeeds('We have a product for family people giving them discounts and low interest loans for home appliances. They can pay us back in small instalments over the course of 4 years')
+
+#We provide a credit card which gives 10% discount on purchasing home appliances and also provides low interest rates based loans