Upload 2 files

backend.py

@@ -8,32 +8,27 @@ import google.generativeai as genai
 
 app = FastAPI()
 
-# Load environment variables and configure Genai
 load_dotenv()
 genai.configure(api_key=os.getenv('GOOGLE_API_KEY'))
 
-# Define the schema for the incoming request
 class Query(BaseModel):
     question: str
     data_source: str
 
 def get_gemini_response(question, prompt):
-    model = genai.GenerativeModel('gemini-1.5-pro') # https://ai.google.dev/pricing?authuser=1#1_5pro
+    model = genai.GenerativeModel('gemini-1.5-pro')
     response = model.generate_content([prompt, question])
     return response.text
 
-# Update column and table names for the new dataset
 sql_cols_human = 'REQUESTID', 'DATETIMEINIT', 'SOURCE', 'DESCRIPTION', 'REQCATEGORY', 'STATUS', 'REFERREDTO', 'DATETIMECLOSED', 'City', 'State', 'Ward', 'Postcode'
 csv_columns_human = ['REQUESTID', 'DATETIMEINIT', 'SOURCE', 'DESCRIPTION', 'REQCATEGORY', 'STATUS', 'REFERREDTO', 'DATETIMECLOSED', 'City', 'State', 'Ward', 'Postcode']
 sql_cols = 'REQUESTID', 'DATETIMEINIT', 'SOURCE', 'DESCRIPTION', 'REQCATEGORY', 'STATUS', 'REFERREDTO', 'DATETIMECLOSED', 'City', 'State', 'Ward', 'Postcode'
-# csv_columns = ["REQUESTID", "DATETIMEINIT",  "SOURCE", "DESCRIPTION", "REQCATEGORY", "STATUS", "REFERREDTO", "DATETIMECLOSED", "PROBADDRESS" "City", "State", "Ward", "Postcode"]
 
 def get_csv_columns():
     df = pd.read_csv('wandsworth_callcenter_sampled.csv')
     return df.columns.tolist()
 
 csv_columns = get_csv_columns()
-print(csv_columns)
 
 sql_prompt = f"""
 You are an expert in converting English questions to SQLite code!
@@ -54,8 +49,6 @@ Also, the SQL code should not have ''' in the beginning or at the end, and SQL w
 Ensure that you only generate valid SQLite database queries, not pandas or Python code.
 """
 
-
-
 csv_prompt = f"""
 You are an expert in analyzing CSV data and converting English questions to pandas query syntax.
 The CSV file is named 'wandsworth_callcenter_sampled.csv' and contains residents' call information in Wandsworth Council.
@@ -78,7 +71,6 @@ Please ensure:
 3. Provide only the pandas query syntax without any additional explanation or markdown formatting.
 Make sure to use only the columns that are available in the CSV file.
 Ensure that you only generate valid pandas queries. NO SQL or other types of code/syntax.
-
 """
 
 def execute_sql_query(query):
@@ -89,9 +81,7 @@ def execute_sql_query(query):
         result = cursor.fetchall()
         return result
     except sqlite3.Error as e:
-        # Capture and explain SQL errors
         sql_error_message = str(e)
-        # Send the error message back to Gemini for explanation        
         error_prompt = f"""
         You are an expert SQL debugger and an assistant of the director. An error occurred while executing the following query:
         {query}
@@ -107,56 +97,30 @@ def execute_sql_query(query):
     finally:
         conn.close()
 
-
-
-
 def execute_pandas_query(query):
     df = pd.read_csv('wandsworth_callcenter_sampled.csv')
-    df.columns = df.columns.str.upper()  # Normalize column names to uppercase
-    print(f"df is loaded. The first line is: {df.head(1)}")
-
-    # Remove code block indicators (e.g., ```python and ```)
+    df.columns = df.columns.str.upper()
     query = query.replace("```python", "").replace("```", "").strip()
-
-    # Split query into lines
-    query_lines = query.split("\n")  # Split into individual statements
+    query_lines = query.split("\n")
     try:
         result = None
-        exec_context = {'df': df, 'pd': pd}  # Execution context for exec()
+        exec_context = {'df': df, 'pd': pd}
         for line in query_lines:
-            line = line.strip()  # Remove extra spaces
-            if line:  # Skip empty lines
-                print(f"Executing line: {line}")
-                exec(line, exec_context)  # Execute each line in the context
-
-        # Retrieve the final result if the last line is a statement
-        result = eval(query_lines[-1].strip(), exec_context)  # Evaluate the last line for the result
-
-        print(f"Query Result Before Serialization: {result}")
-
-        # Handle DataFrame results
+            line = line.strip()
+            if line:
+                exec(line, exec_context)
+        result = eval(query_lines[-1].strip(), exec_context)
         if isinstance(result, pd.DataFrame):
-            # Replace NaN and infinite values with JSON-compliant values
             result = result.replace([float('inf'), -float('inf')], None).fillna(value="N/A")
             return result.to_dict(orient='records')
-
-        # Handle Series results
         elif isinstance(result, pd.Series):
             result = result.replace([float('inf'), -float('inf')], None).fillna(value="N/A")
             return result.to_dict()
-
-        # Handle scalar results
         else:
             return result
-
     except Exception as e:
-        print(f"Error: {e}")
         raise HTTPException(status_code=400, detail=f"Pandas Error: {str(e)}")
 
-
-
-
-
 @app.post("/query")
 async def process_query(query: Query):
     if query.data_source == "SQL Database":
@@ -167,9 +131,8 @@ async def process_query(query: Query):
         except HTTPException as e:
             error_detail = e.detail
             return {"query": ai_response, "error": error_detail["error"], "explanation": error_detail["explanation"]}
-    else:  # CSV Data
+    else:
         ai_response = get_gemini_response(query.question, csv_prompt)
-        print(f"\n\nai_response: {ai_response}")
         try:
             result = execute_pandas_query(ai_response)
             return {"query": ai_response, "result": result, "columns": csv_columns}

frontend.py

@@ -2,30 +2,26 @@ import streamlit as st
 import requests
 import pandas as pd
 
-# Page Configuration
 st.set_page_config(
-    page_title="CallDataAI - Wandsworth Council Call Center Analysis",
+    page_title="CallDataAI - Wandsworth Council NetCall Analysis",
     page_icon="📞",
     layout="wide",
     initial_sidebar_state="expanded",
 )
 
-# Sidebar
 st.sidebar.title("📞 CallDataAI")
 st.sidebar.markdown(
     """
-    **Welcome to CallDataAI**, your AI-powered assistant for analyzing Wandsworth Council's Call Center data. Use the menu below to:
+    **Welcome to CallDataAI**, your AI-powered assistant for analyzing Wandsworth Council's NetCall data. Use the menu below to:
     - Select the data source (SQL/CSV)
     - Run pre-defined or custom queries
     - Gain actionable insights
     """
 )
 
-# Data source selection
 st.sidebar.markdown("### Select Data Source:")
 data_source = st.sidebar.radio("", ('SQL Database', 'CSV Database'))
 
-# Common queries section
 st.sidebar.markdown("### Common Queries:")
 common_queries = {
     'SQL Database': [
@@ -45,22 +41,17 @@ common_queries = {
 }
 
 for idx, query in enumerate(common_queries[data_source]):
-    if st.sidebar.button(query, key=f"query_button_{idx}"):  # Add unique key
+    if st.sidebar.button(query, key=f"query_button_{idx}"):
         st.session_state["common_query"] = query
 
-
-
-
-# Title and Description
-st.title("📞 CallDataAI - Wandsworth Council Call Center Analysis")
+st.title("📞 CallDataAI - Wandsworth Council NetCall Analysis")
 st.markdown(
     """
-    **CallDataAI** is an AI-powered chatbot designed for analyzing Wandsworth Council's Call Center data.
+    **CallDataAI** is an AI-powered chatbot designed for analyzing Wandsworth Council's NetCall data.
     Input natural language queries to explore the data and gain actionable insights.
     """
 )
 
-# Input Section
 with st.container():
     st.markdown("### Enter Your Question")
     question = st.text_input(
@@ -68,24 +59,19 @@ with st.container():
     )
     submit = st.button("Submit", type="primary")
 
-# Main Content
 if submit:
-    # Send request to FastAPI backend
     with st.spinner("Processing your request..."):
         response = requests.post(
             "http://localhost:8000/query", json={"question": question, "data_source": data_source}
         )
 
-    # Handle response
     if response.status_code == 200:
         data = response.json()
 
-        # Error Handling
         if "error" in data:
             with st.expander("Error Explanation"):
                 st.error(data["explanation"])
 
-        # Display Results
         else:
             col1, col2 = st.columns(2)
 
@@ -117,7 +103,6 @@ if submit:
                 st.markdown("### Available CSV Columns")
                 st.write(data["columns"])
 
-            # Update chat history in session state
             if "chat_history" not in st.session_state:
                 st.session_state["chat_history"] = []
 
@@ -127,7 +112,6 @@ if submit:
     else:
         st.error(f"Error processing your request: {response.text}")
 
-# Chat History Section
 with st.container():
     st.markdown("### Chat History")
     if "chat_history" in st.session_state: