Update src/streamlit_app.py

src/streamlit_app.py

@@ -51,25 +51,9 @@ def load_data():
 def extract_numeric_ram(ram) -> Optional[int]:
     if pd.isna(ram):
         return None
-def calculate_quantized_sizes(model_size_str: str) -> Tuple[str, str, str]:
-    """
-    Given a model size like '13GB', calculate its 8-bit and 4-bit equivalents.
-    Returns a tuple of (FP16, 8-bit, 4-bit) sizes as strings.
-    """
-    try:
-        size_str = model_size_str.lower().strip()
-        if "mb" in size_str:
-            num = float(size_str.replace("mb", "")) / 1024  # Convert MB to GB
-        else:
-            num = float(size_str.replace("gb", ""))
-
-        fp16 = num
-        q8 = fp16 * 0.5
-        q4 = fp16 * 0.25
-        return f"{fp16:.1f} GB", f"{q8:.1f} GB", f"{q4:.1f} GB"
-    except:
-        return model_size_str, "-", "-"
-
+    
+    ram_str = str(ram).lower().replace(" ", "")
+    
     # Handle various formats: "8GB", "8 GB", "8gb", "8192MB", etc.
     gb_match = re.search(r"(\d+(?:\.\d+)?)(?:gb|g)", ram_str)
     if gb_match:
@@ -321,7 +305,7 @@ def display_model_categories(models_dict: Dict[str, List[Dict]], ram_gb: int):
                     with col1:
                         st.markdown(f"**{model['name']}**")
                     with col2:
-                        st.markdown(f"`{model['size']}`")
+                        st.markdown(f"{model['size']}")
                     with col3:
                         st.markdown(f"*{model['description']}*")
 
@@ -606,6 +590,7 @@ if selected_ram_key in LLM_DATABASE and selected_category in LLM_DATABASE[select
                 st.caption("Download Size")
             with col3:
                 st.markdown(f"*{model['description']}*")
+                # Add download suggestion
                 if "Llama" in model['name']:
                     st.caption("🔗 Available on Hugging Face & Ollama")
                 elif "Mistral" in model['name']:
@@ -614,24 +599,10 @@ if selected_ram_key in LLM_DATABASE and selected_category in LLM_DATABASE[select
                     st.caption("🔗 Available on Hugging Face & Google")
                 else:
                     st.caption("🔗 Available on Hugging Face")
-            
-            # 🔽 Quantization size details
-            fp16, q8, q4 = calculate_quantized_sizes(model['size'])
-            with st.expander("💾 Quantized Size Estimates"):
-                st.markdown(f"""
-                | Format | Size |
-                |--------|------|
-                | FP16 (Full Precision) | **{fp16}** |
-                | 8-bit Quantized       | **{q8}** |
-                | 4-bit Quantized       | **{q4}** |
-                """)
-    
-    st.markdown("---")  # ✅ this belongs inside the if block
-
+            st.markdown("---")
 else:
     st.info(f"No {selected_category} models available for {selected_ram_range}")
 
-
 # Enhanced reference guide
 with st.expander("📘 Model Guide & Download Information"):
     st.markdown("""
@@ -688,10 +659,10 @@ with st.expander("📘 Model Guide & Download Information"):
     - **AWQ**: Advanced weight quantization
     
     ### **Download Tips**
-    - Use `git lfs` for large models from Hugging Face
+    - Use git lfs for large models from Hugging Face
     - Consider bandwidth and storage before downloading
     - Start with 4-bit quantized versions for testing
-    - Use `ollama pull model_name` for easiest setup
+    - Use ollama pull model_name for easiest setup
     
     ## 🔧 Optimization Strategies
     
@@ -711,7 +682,7 @@ st.markdown("---")
 st.markdown("""
 ### 🔗 Essential Download & Deployment Tools
 **📦 Easy Model Deployment:**
-- [**Ollama**](https://ollama.ai/) – `curl -fsSL https://ollama.ai/install.sh | sh`
+- [**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:**