Update src/streamlit_app.py

src/streamlit_app.py

@@ -8,6 +8,7 @@ Requirements: streamlit, pandas, plotly, openpyxl
 
 import streamlit as st
 import pandas as pd
+import numpy as np
 import re
 import plotly.express as px
 import plotly.graph_objects as go
@@ -329,6 +330,30 @@ def generate_demo_data():
     }
     return pd.DataFrame(demo_data)
 
+# Function to safely prepare user options
+def prepare_user_options(df):
+    """Safely prepare user options for selectbox, handling NaN values and mixed types"""
+    try:
+        # Get unique names and filter out NaN values
+        unique_names = df["Full Name"].dropna().unique()
+        
+        # Convert to strings and filter out any remaining non-string values
+        valid_names = []
+        for name in unique_names:
+            try:
+                str_name = str(name).strip()
+                if str_name and str_name.lower() != 'nan':
+                    valid_names.append(str_name)
+            except:
+                continue
+        
+        # Create options list with proper string concatenation
+        options = ["Select a student..."] + sorted(valid_names)
+        return options
+    except Exception as e:
+        st.error(f"Error preparing user options: {e}")
+        return ["Select a student..."]
+
 # Main App
 st.title("🧠 LLM Compatibility Advisor")
 st.markdown("Get personalized recommendations from **150+ popular open source AI models** with download sizes!")
@@ -360,6 +385,10 @@ if missing_columns:
     st.info("Please ensure your Excel file contains the required columns.")
     st.stop()
 
+# Clean the dataframe
+df = df.copy()
+df["Full Name"] = df["Full Name"].astype(str).str.strip()
+
 # Sidebar filters and info
 with st.sidebar:
     st.header("🔍 Filters & Info")
@@ -393,57 +422,64 @@ with st.sidebar:
     if not pd.isna(avg_mobile_ram):
         st.metric("Avg Mobile RAM", f"{avg_mobile_ram:.1f} GB")
 
-# User selection with search
+# User selection with search - FIXED VERSION
 st.subheader("👤 Individual Student Analysis")
+
+# Prepare options safely
+user_options = prepare_user_options(df)
+
 selected_user = st.selectbox(
     "Choose a student:",
-    options=[""] + list(df["Full Name"].unique()),
-    format_func=lambda x: "Select a student..." if x == "" else x
+    options=user_options,
+    index=0  # Default to first option ("Select a student...")
 )
 
-if selected_user:
-    user_data = df[df["Full Name"] == selected_user].iloc[0]
-    
-    # Enhanced user display
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.markdown("### 💻 Laptop Configuration")
-        laptop_os_icon, laptop_os_name = get_os_info(user_data.get('Laptop Operating System'))
-        laptop_ram = user_data.get('Laptop RAM', 'Not specified')
-        laptop_rec, laptop_tier, laptop_info, laptop_models = recommend_llm(laptop_ram)
-        laptop_ram_gb = extract_numeric_ram(laptop_ram) or 0
-        
-        st.markdown(f"**OS:** {laptop_os_icon} {laptop_os_name}")
-        st.markdown(f"**RAM:** {laptop_ram}")
-        st.markdown(f"**Performance Tier:** {laptop_tier}")
+if selected_user and selected_user != "Select a student...":
+    # Find user data with safe lookup
+    user_data_mask = df["Full Name"].astype(str).str.strip() == selected_user
+    if user_data_mask.any():
+        user_data = df[user_data_mask].iloc[0]
         
-        st.success(f"**💡 Recommendation:** {laptop_rec}")
-        st.info(f"**ℹ️ Notes:** {laptop_info}")
+        # Enhanced user display
+        col1, col2 = st.columns(2)
         
-        # Display detailed models for laptop
-        if laptop_models:
-            filtered_models = {k: v for k, v in laptop_models.items() if k in show_categories}
-            display_model_categories(filtered_models, laptop_ram_gb)
-    
-    with col2:
-        st.markdown("### 📱 Mobile Configuration")
-        mobile_os_icon, mobile_os_name = get_os_info(user_data.get('Mobile Operating System'))
-        mobile_ram = user_data.get('Mobile RAM', 'Not specified')
-        mobile_rec, mobile_tier, mobile_info, mobile_models = recommend_llm(mobile_ram)
-        mobile_ram_gb = extract_numeric_ram(mobile_ram) or 0
+        with col1:
+            st.markdown("### 💻 Laptop Configuration")
+            laptop_os_icon, laptop_os_name = get_os_info(user_data.get('Laptop Operating System'))
+            laptop_ram = user_data.get('Laptop RAM', 'Not specified')
+            laptop_rec, laptop_tier, laptop_info, laptop_models = recommend_llm(laptop_ram)
+            laptop_ram_gb = extract_numeric_ram(laptop_ram) or 0
+            
+            st.markdown(f"**OS:** {laptop_os_icon} {laptop_os_name}")
+            st.markdown(f"**RAM:** {laptop_ram}")
+            st.markdown(f"**Performance Tier:** {laptop_tier}")
+            
+            st.success(f"**💡 Recommendation:** {laptop_rec}")
+            st.info(f"**ℹ️ Notes:** {laptop_info}")
+            
+            # Display detailed models for laptop
+            if laptop_models:
+                filtered_models = {k: v for k, v in laptop_models.items() if k in show_categories}
+                display_model_categories(filtered_models, laptop_ram_gb)
         
-        st.markdown(f"**OS:** {mobile_os_icon} {mobile_os_name}")
-        st.markdown(f"**RAM:** {mobile_ram}")
-        st.markdown(f"**Performance Tier:** {mobile_tier}")
-        
-        st.success(f"**💡 Recommendation:** {mobile_rec}")
-        st.info(f"**ℹ️ Notes:** {mobile_info}")
-        
-        # Display detailed models for mobile
-        if mobile_models:
-            filtered_models = {k: v for k, v in mobile_models.items() if k in show_categories}
-            display_model_categories(filtered_models, mobile_ram_gb)
+        with col2:
+            st.markdown("### 📱 Mobile Configuration")
+            mobile_os_icon, mobile_os_name = get_os_info(user_data.get('Mobile Operating System'))
+            mobile_ram = user_data.get('Mobile RAM', 'Not specified')
+            mobile_rec, mobile_tier, mobile_info, mobile_models = recommend_llm(mobile_ram)
+            mobile_ram_gb = extract_numeric_ram(mobile_ram) or 0
+            
+            st.markdown(f"**OS:** {mobile_os_icon} {mobile_os_name}")
+            st.markdown(f"**RAM:** {mobile_ram}")
+            st.markdown(f"**Performance Tier:** {mobile_tier}")
+            
+            st.success(f"**💡 Recommendation:** {mobile_rec}")
+            st.info(f"**ℹ️ Notes:** {mobile_info}")
+            
+            # Display detailed models for mobile
+            if mobile_models:
+                filtered_models = {k: v for k, v in mobile_models.items() if k in show_categories}
+                display_model_categories(filtered_models, mobile_ram_gb)
 
 # Batch Analysis Section
 st.markdown("---")
@@ -589,7 +625,7 @@ with st.expander("📘 Model Guide & Download Information"):
     - **Zephyr**: HuggingFace's chat specialist
     - **OpenChat**: High-quality conversation models
     - **Neural-Chat**: Intel-optimized chat models
-    
+ 
     ### 🧮 **Reasoning Masters**
     - **WizardMath**: Mathematical problem solving
     - **MetaMath**: Advanced arithmetic reasoning