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import streamlit as st
import pandas as pd
import plotly.express as px
import base64
import ollama
import os
# Load the uploaded customer data file
uploaded_file = st.sidebar.file_uploader("Upload Customer Data (Excel):", type=["xlsx"])
if uploaded_file:
customer_data = pd.read_excel(uploaded_file)
st.sidebar.success("Customer data loaded successfully!")
# Display dataset overview
st.subheader("Customer Data Overview")
st.dataframe(customer_data)
# Sidebar filters
st.sidebar.header("Customer Data Filters")
region_filter = st.sidebar.multiselect("Filter by Region:", customer_data["Region"].unique(), default=customer_data["Region"].unique())
category_filter = st.sidebar.multiselect("Filter by Product Category:", customer_data["Product_Category"].unique(), default=customer_data["Product_Category"].unique())
filtered_data = customer_data[(customer_data["Region"].isin(region_filter)) &
(customer_data["Product_Category"].isin(category_filter))]
# Generate data summary
def generate_data_summary(data):
sales_summary = data.groupby("Product_Category")["Sales_Amount"].sum().to_dict()
region_summary = data.groupby("Region")["Sales_Amount"].sum().to_dict()
churn_distribution = data["Churn_Risk"].value_counts().to_dict()
satisfaction_avg = data.groupby("Region")["Satisfaction_Score"].mean().to_dict()
summary = {
"Total Sales by Product Category": sales_summary,
"Total Sales by Region": region_summary,
"Churn Risk Distribution": churn_distribution,
"Average Satisfaction Score by Region": satisfaction_avg,
"Total Number of Records": data
}
return summary
data_summary = generate_data_summary(filtered_data)
# Sales Analysis
st.subheader("Sales Analysis")
sales_by_category = filtered_data.groupby("Product_Category")["Sales_Amount"].sum().reset_index()
fig_sales_category = px.bar(sales_by_category, x="Product_Category", y="Sales_Amount", title="Sales by Product Category")
st.plotly_chart(fig_sales_category)
sales_by_region = filtered_data.groupby("Region")["Sales_Amount"].sum().reset_index()
fig_sales_region = px.bar(sales_by_region, x="Region", y="Sales_Amount", title="Sales by Region")
st.plotly_chart(fig_sales_region)
# AI-Powered Chatbot Section
st.subheader("AI-Powered Chatbot")
if "chat_history" not in st.session_state:
st.session_state["chat_history"] = []
user_question = st.text_input("Ask a question:")
# Button to process the question
if st.button("Ask AI"):
if user_question:
with st.spinner("Generating response, please wait..."):
try:
# Include data summary in the context
summary_context ="\n".join([f"{key}: {value}" for key, value in data_summary.items()])
response = ollama.chat(model="llama3.2-vision",
messages=[
{"role": "system", "content": "You are an AI assistant. Use the data context provided to answer questions."},
{"role": "user", "content": f"Data Context:\n{summary_context}"},
{"role": "user", "content": f"Question: {user_question}"}])
# Append user question and AI response to the chat history
st.session_state["chat_history"].append({
"question": user_question,
"answer": response["message"]["content"]
})
except Exception as e:
st.error(f"An error occurred: {e}")
else:
st.warning("Please enter a question before clicking the button.")
# Display chat history
st.subheader("Chat History")
for chat in st.session_state["chat_history"]:
st.markdown(f"**You:** {chat['question']}")
st.markdown(f"**vivekda05** {chat['answer']}")
script_dir = os.path.dirname(os.path.abspath(__file__))
app_path = os.path.join(script_dir, "aptemp.py")
# Write the embedded app code to app.py
with open(app_path, "w") as app_file:
app_file.write(app_path)
# Command to run the Streamlit application
command = f"streamlit run apps1.py"
# Execute the command
os.system(command) |