Money needs integrated with utils

app.py

@@ -4,6 +4,7 @@ import google.generativeai as genai
 GOOGLE_API_KEY=os.getenv('GEMINI_API_KEY')
 genai.configure(api_key=GOOGLE_API_KEY)
 model = genai.GenerativeModel(model_name = "gemini-pro")
+from utils import findTop3MoneyNeeds
 
 
 # Create a banner using Markdown
@@ -86,6 +87,9 @@ if submit_button:
     proposal = proposal.format(selectedCity, selectedProduct, userProposal)
     st.write("Entered proposal:", proposal)
     st.write("Analyzing your proposition")
+    
+    topMoneyNeeds = findTop3MoneyNeeds(moneyNeeds)
+
     response = model.generate_content([pre_prompt.format(proposal)])
     st.write(response.text)
 

db.py

@@ -0,0 +1,36 @@
+import sqlite3
+
+def fetch_db_rows_as_dicts(db_path, table_name):
+    try:
+        # Connect to the SQLite database
+        conn = sqlite3.connect(db_path)
+        conn.row_factory = sqlite3.Row  # This allows us to access columns by name
+        cursor = conn.cursor()
+
+        # Get the column names
+        cursor.execute(f"PRAGMA table_info({table_name});")
+        columns_info = cursor.fetchall()
+        column_names = [col[1] for col in columns_info]
+        
+        # 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()
+        
+        assert len(rows) > 1
+
+        return column_names, rows[1:]
+            
+    except sqlite3.Error as e:
+        print(f"SQLite error: {e}")
+    finally:
+        # Close the connection
+        if conn:
+            conn.close()
+
+
+
+# Example usage:
+# fetch_db_rows_as_dicts('money_needs.sqlite', 'money_needs')

requirements.txt

@@ -1,4 +1,5 @@
 streamlit
 altair==4.0
 transformers
-google-generativeai
+google-generativeai
+db-sqlite3

utils.py

@@ -0,0 +1,84 @@
+from db import fetch_db_rows_as_dicts
+import google.generativeai as genai
+import json
+
+GOOGLE_API_KEY='AIzaSyC0T2fN5Dga-6nkPc6HYV7-bDZskqgALX0'
+genai.configure(api_key=GOOGLE_API_KEY)
+model = genai.GenerativeModel(model_name = "gemini-pro")
+
+def load_json_from_string(json_string):
+    try:
+        data = json.loads(json_string)
+        return data
+    except json.JSONDecodeError as e:
+        print(f"Error decoding JSON: {e}")
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+def concatenate_keys(keys):
+    concatenated_string = ""
+    for i, d in enumerate(keys, start=1):
+        concatenated_string += f"{d} "
+    return concatenated_string.strip()
+
+def transform_to_dict_of_dicts(columns, rows):
+    # Initialize the result dictionary
+    result = {}
+
+    # Iterate over each row
+    for row in rows:
+        # 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 findTop3MoneyNeeds(proposition):
+    moneyNeeds, rows = fetch_db_rows_as_dicts('money_needs.sqlite', 'money_needs')
+    moneyNeedsDict = transform_to_dict_of_dicts(moneyNeeds, rows)
+    print(list(moneyNeedsDict.keys()))
+    needs = findTop3Needs(proposition, list(moneyNeedsDict.keys()))
+    needDictIndexes = []
+    for need in needs:
+        needDictIndexes.append(moneyNeedsDict[need])
+
+    print(needDictIndexes)
+    return 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']
+
+
+#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')