Update app.py

app.py

@@ -1,73 +1,73 @@
-import streamlit as st
-import pandas as pd
-import openai
-import os
-from dotenv import load_dotenv
-
-# Load API key from .env
-load_dotenv()
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-# Load CSV file
-df = pd.read_csv(r"C:\Users\sadac\OneDrive\Desktop\churn-chatbot\churn_predictions.csv")
-
-# Streamlit UI
-st.set_page_config(page_title="Customer Churn Chatbot")
-st.title("Customer Churn Chatbot")
-st.markdown("Ask if a customer is predicted to churn and get GPT-based suggestions.")
-
-# Input fields
-customer_id = st.text_input("Enter Customer ID (optional):")
-customer_name = st.text_input("Enter Customer Name (optional):")
-
-if st.button("Check Churn"):
-    result = pd.DataFrame()
-    
-    # Search by ID
-    if customer_id:
-        try:
-            customer_id = int(customer_id)
-            result = df[df["CustomerId"] == customer_id]
-        except ValueError:
-            st.error("Customer ID must be an integer.")
-    
-    # Search by name
-    elif customer_name:
-        result = df[df["Customer"].str.lower() == customer_name.lower()]
-
-    if not result.empty:
-        row = result.iloc[0]
-        churn = row["PredictedChurn"]
-
-        # Basic suggestion
-        suggestion = (
-            "High risk of churn. Consider giving discounts, loyalty rewards."
-            if churn == 1 else
-            "Customer is stable. Maintain good service."
-        )
-
-        # GPT prompt
-        prompt = (
-            f"Customer: {row['Customer']}\n"
-            f"Churn Prediction: {'Yes' if churn else 'No'}\n"
-            "What actions should be taken to reduce churn or maintain retention?"
-        )
-
-        with st.spinner("Asking GPT..."):
-            try:
-                response = openai.ChatCompletion.create(
-                    model="gpt-4",  # or "gpt-3.5-turbo" if needed
-                    messages=[{"role": "user", "content": prompt}]
-                )
-                gpt_reply = response.choices[0].message["content"]
-            except Exception as e:
-                gpt_reply = f" GPT request failed: {e}"
-
-        # Display output
-        st.success(f"Churn: {'Yes' if churn else 'No'}")
-        st.info(f"Suggestion: {suggestion}")
-        st.markdown(" GPT's Advice:")
-        st.write(gpt_reply)
-
-    else:
-        st.warning("Customer not found. Please check your input.")
+import streamlit as st
+import pandas as pd
+import openai
+import os
+from dotenv import load_dotenv
+
+# Load API key from .env
+load_dotenv()
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Load CSV file
+df = pd.read_csv('churn_predictions.csv')
+
+# Streamlit UI
+st.set_page_config(page_title="Customer Churn Chatbot")
+st.title("Customer Churn Chatbot")
+st.markdown("Ask if a customer is predicted to churn and get GPT-based suggestions.")
+
+# Input fields
+customer_id = st.text_input("Enter Customer ID (optional):")
+customer_name = st.text_input("Enter Customer Name (optional):")
+
+if st.button("Check Churn"):
+    result = pd.DataFrame()
+    
+    # Search by ID
+    if customer_id:
+        try:
+            customer_id = int(customer_id)
+            result = df[df["CustomerId"] == customer_id]
+        except ValueError:
+            st.error("Customer ID must be an integer.")
+    
+    # Search by name
+    elif customer_name:
+        result = df[df["Customer"].str.lower() == customer_name.lower()]
+
+    if not result.empty:
+        row = result.iloc[0]
+        churn = row["PredictedChurn"]
+
+        # Basic suggestion
+        suggestion = (
+            "High risk of churn. Consider giving discounts, loyalty rewards."
+            if churn == 1 else
+            "Customer is stable. Maintain good service."
+        )
+
+        # GPT prompt
+        prompt = (
+            f"Customer: {row['Customer']}\n"
+            f"Churn Prediction: {'Yes' if churn else 'No'}\n"
+            "What actions should be taken to reduce churn or maintain retention?"
+        )
+
+        with st.spinner("Asking GPT..."):
+            try:
+                response = openai.ChatCompletion.create(
+                    model="gpt-4",  # or "gpt-3.5-turbo" if needed
+                    messages=[{"role": "user", "content": prompt}]
+                )
+                gpt_reply = response.choices[0].message["content"]
+            except Exception as e:
+                gpt_reply = f" GPT request failed: {e}"
+
+        # Display output
+        st.success(f"Churn: {'Yes' if churn else 'No'}")
+        st.info(f"Suggestion: {suggestion}")
+        st.markdown(" GPT's Advice:")
+        st.write(gpt_reply)
+
+    else:
+        st.warning("Customer not found. Please check your input.")