my files

apps.py

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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)

apps1.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import base64
+import ollama
+
+# 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']}")

aptemp.py

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+C:\Users\Vivek\PycharmProjects\PythonProject1\aptemp.py