Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
 LLM Compatibility Advisor - Streamlined with Download Sizes
-Author: Assistant2
+Author: Assistant
 Description: Provides device-based LLM recommendations with popular models and download sizes
 Requirements: streamlit, pandas, plotly, openpyxl
 """
@@ -21,30 +21,35 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# Enhanced data loading with error handling
+# Enhanced data loading with error handling - FIXED FILE PATH
 @st.cache_data
 def load_data():
     try:
-        # Use actual file paths
-        df1 = pd.read_excel("src/BITS_INTERNS.xlsx", sheet_name="Form Responses 1")
-        df2 = pd.read_excel("/mnt/data/Summer of AI - ICFAI  (Responses) (3).xlsx")
-
-        df1.columns = df1.columns.str.strip()
-        df2.columns = df2.columns.str.strip()
+        # Try multiple possible file locations
+        possible_paths = [
+            "BITS_INTERNS.xlsx",  # Current directory
+            "src/BITS_INTERNS.xlsx",  # src subdirectory
+            "./BITS_INTERNS.xlsx",  # Explicit current directory
+        ]
+        
+        df = None
+        for path in possible_paths:
+            try:
+                df = pd.read_excel(path, sheet_name="Form Responses 1")
+                st.info(f"✅ Successfully loaded data from: {path}")
+                break
+            except FileNotFoundError:
+                continue
+        
+        if df is None:
+            return None, "Excel file 'BITS_INTERNS.xlsx' not found in any expected location. Please ensure the file is in the same directory as this script."
+        
+        df.columns = df.columns.str.strip()
+        return df, None
         
-        # Find common columns and combine
-        common_cols = list(set(df1.columns) & set(df2.columns))
-        df1 = df1[common_cols]
-        df2 = df2[common_cols]
-
-        combined_df = pd.concat([df1, df2], ignore_index=True)
-        return combined_df, None
-    except FileNotFoundError as e:
-        return None, f"File not found: {e}"
     except Exception as e:
         return None, f"Error loading data: {str(e)}"
 
-
 # Enhanced RAM extraction with better parsing
 def extract_numeric_ram(ram) -> Optional[int]:
     if pd.isna(ram):
@@ -307,20 +312,58 @@ def display_model_categories(models_dict: Dict[str, List[Dict]], ram_gb: int):
                     with col3:
                         st.markdown(f"*{model['description']}*")
 
+# File upload fallback
+def show_file_upload():
+    """Show file upload option when Excel file is not found"""
+    st.warning("📁 Excel file not found. Please upload your data file:")
+    uploaded_file = st.file_uploader(
+        "Upload BITS_INTERNS.xlsx", 
+        type=['xlsx', 'xls'],
+        help="Upload the Excel file containing student data"
+    )
+    
+    if uploaded_file is not None:
+        try:
+            df = pd.read_excel(uploaded_file, sheet_name="Form Responses 1")
+            df.columns = df.columns.str.strip()
+            st.success("✅ File uploaded successfully!")
+            return df, None
+        except Exception as e:
+            return None, f"Error reading uploaded file: {str(e)}"
+    
+    return None, "Please upload the Excel file to continue."
+
 # 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 fallback to file upload
 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 error and df is None:
+    # Show file upload option
+    df, upload_error = show_file_upload()
+    if upload_error:
+        st.error(upload_error)
+        st.info("📋 **To fix this issue:**\n1. Ensure 'BITS_INTERNS.xlsx' is in the same directory as this script\n2. Or use the file upload option above\n3. Check that the Excel file has a sheet named 'Form Responses 1'")
+        
+        # Show demo mode option
+        if st.button("🚀 Try Demo Mode"):
+            # Create sample data for demonstration
+            demo_data = {
+                "Full Name": ["John Doe", "Jane Smith", "Alex Johnson", "Sarah Wilson"],
+                "Laptop RAM": ["8GB", "16GB", "4GB", "32GB"],
+                "Mobile RAM": ["6GB", "8GB", "4GB", "12GB"],
+                "Laptop Operating System": ["Windows 11", "macOS", "Ubuntu Linux", "Windows 10"],
+                "Mobile Operating System": ["Android", "iOS", "Android", "iOS"]
+            }
+            df = pd.DataFrame(demo_data)
+            st.success("🔥 Demo mode activated! Using sample data.")
+        else:
+            st.stop()
 
 if df is None or df.empty:
-    st.error("No data found in the Excel file.")
+    st.error("No data found in the file.")
     st.stop()
 
 # Sidebar filters and info
@@ -426,7 +469,7 @@ 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]
@@ -481,7 +524,7 @@ with explorer_col1:
     selected_ram_range = st.selectbox(
         "Select RAM range to explore models:",
         ["≤2GB (Ultra Low)", "3-4GB (Low)", "5-6GB (Moderate-Low)", 
-        "7-8GB (Moderate)", "9-16GB (Good)", "17-32GB (High)", ">32GB (Ultra High)"]
+         "7-8GB (Moderate)", "9-16GB (Good)", "17-32GB (High)", ">32GB (Ultra High)"]
     )
 
 with explorer_col2:
@@ -591,7 +634,7 @@ with st.expander("📘 Model Guide & Download Information"):
     - Consider bandwidth and storage before downloading
     - Start with 4-bit quantized versions for testing
     - Use `ollama pull model_name` for easiest setup
-    
+      
     ## 🔧 Optimization Strategies
     
     ### **Memory Reduction**
@@ -609,17 +652,13 @@ 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
-
-
 ---
 """)