Update src/streamlit_app.py

src/streamlit_app.py

@@ -39,6 +39,12 @@ def load_data():
             return None, f"Excel file '{path}' not found. Please upload the file."
         except Exception as e:
             return None, f"Error loading '{path}': {str(e)}"
+    
+    # Return success case - this was missing!
+    if combined_df.empty:
+        return None, "No data found in Excel files."
+    else:
+        return combined_df, None
 
 # Enhanced RAM extraction with better parsing
 def extract_numeric_ram(ram) -> Optional[int]:
@@ -302,20 +308,56 @@ def display_model_categories(models_dict: Dict[str, List[Dict]], ram_gb: int):
                     with col3:
                         st.markdown(f"*{model['description']}*")
 
+# Demo data generator for when Excel files are not available
+def generate_demo_data():
+    """Generate demo data for testing when Excel files are missing"""
+    demo_data = {
+        "Full Name": [
+            "Demo Student 1", "Demo Student 2", "Demo Student 3", "Demo Student 4",
+            "Demo Student 5", "Demo Student 6", "Demo Student 7", "Demo Student 8"
+        ],
+        "Laptop RAM": ["8GB", "16GB", "4GB", "32GB", "6GB", "12GB", "2GB", "24GB"],
+        "Mobile RAM": ["4GB", "8GB", "3GB", "12GB", "6GB", "4GB", "2GB", "8GB"],
+        "Laptop Operating System": [
+            "Windows 11", "macOS Monterey", "Ubuntu 22.04", "Windows 10",
+            "macOS Big Sur", "Fedora 36", "Windows 11", "macOS Ventura"
+        ],
+        "Mobile Operating System": [
+            "Android 13", "iOS 16", "Android 12", "iOS 15",
+            "Android 14", "iOS 17", "Android 11", "iOS 16"
+        ]
+    }
+    return pd.DataFrame(demo_data)
+
 # Main App
 st.title("🧠 LLM Compatibility Advisor")
 st.markdown("Get personalized recommendations from **150+ popular open source AI models** with download sizes!")
 
-# Load data
+# Load data with better error handling
 df, error = load_data()
 
-if error:
-    st.error(error)
-    st.info("Please ensure the Excel file 'BITS_INTERNS.xlsx' is in the same directory as this script.")
-    st.stop()
-
-if df is None or df.empty:
-    st.error("No data found in the Excel file.")
+if error or df is None or df.empty:
+    st.warning("⚠️ Excel files not found. Running with demo data for testing.")
+    st.info("📁 To use real data, place 'BITS_INTERNS.xlsx' and 'Summer of AI - ICFAI  (Responses) (3).xlsx' in the 'src/' directory.")
+    df = generate_demo_data()
+    
+    with st.expander("📋 Expected Data Format"):
+        st.markdown("""
+        The app expects Excel files with the following columns:
+        - **Full Name**: Student name
+        - **Laptop RAM**: RAM specification (e.g., "8GB", "16 GB", "8192MB")
+        - **Mobile RAM**: Mobile device RAM
+        - **Laptop Operating System**: OS name
+        - **Mobile Operating System**: Mobile OS name
+        """)
+
+# Verify required columns exist
+required_columns = ["Full Name", "Laptop RAM", "Mobile RAM"]
+missing_columns = [col for col in required_columns if col not in df.columns]
+
+if missing_columns:
+    st.error(f"Missing required columns: {missing_columns}")
+    st.info("Please ensure your Excel file contains the required columns.")
     st.stop()
 
 # Sidebar filters and info
@@ -421,9 +463,8 @@ df_display["Mobile LLM"] = mobile_recommendations
 df_display["Laptop Tier"] = laptop_tiers
 df_display["Mobile Tier"] = mobile_tiers
 
-# Filter based on sidebar selections (RAM range filter removed)
+# Filter based on sidebar selections
 mask = (laptop_tiers.isin(performance_filter) | mobile_tiers.isin(performance_filter))
-
 df_filtered = df_display[mask]
 
 # Display filtered table
@@ -604,17 +645,13 @@ with st.expander("📘 Model Guide & Download Information"):
 st.markdown("---")
 st.markdown("""
 ### 🔗 Essential Download & Deployment Tools
-
 **📦 Easy Model Deployment:**
 - [**Ollama**](https://ollama.ai/) – `curl -fsSL https://ollama.ai/install.sh | sh`
 - [**LM Studio**](https://lmstudio.ai/) – Drag-and-drop GUI for running models locally
 - [**GPT4All**](https://gpt4all.io/) – Cross-platform desktop app for local LLMs
-
 **🤗 Model Repositories:**
 - [**Hugging Face Hub**](https://huggingface.co/models) – Filter by model size, task, and license
 - [**TheBloke's Quantizations**](https://huggingface.co/TheBloke) – Pre-quantized models in GGUF/GPTQ format
 - [**Awesome LLM**](https://github.com/Hannibal046/Awesome-LLMs) – Curated list of models and resources
-
-
 ---
 """)