About section enhanced

app.py

@@ -4,11 +4,12 @@ import tokonomics
 from utils import create_model_hierarchy
 from utils_on import analyze_hf_model  # New import for On Premise Estimator functionality
 
-st.set_page_config(page_title="LLM Pricing App", layout="wide")
+st.set_page_config(page_title="LLM Pricing Calculator", layout="wide")
 
 # --------------------------
 # Async Data Loading Function
 # --------------------------
+
 async def load_data():
     """Simulate loading data asynchronously."""
     AVAILABLE_MODELS = await tokonomics.get_available_models()
@@ -123,16 +124,16 @@ def format_analysis_report(analysis_result: dict) -> str:
         lines.append("- None found")
     lines.append(f"\n**Largest Compatible GPU:** {analysis_result.get('largest_compatible_gpu', 'N/A')}\n")
     
-    gpu_perf = analysis_result.get("gpu_performance", {})
-    if gpu_perf:
-        lines.append("#### GPU Performance:")
-        for gpu, perf in gpu_perf.items():
-            lines.append(f"**{gpu}:**")
-            lines.append(f"  - Tokens per Second: {perf.get('tokens_per_second', 0):.2f}")
-            lines.append(f"  - FLOPs per Token: {perf.get('flops_per_token', 0):.2f}")
-            lines.append(f"  - Effective TFLOPS: {perf.get('effective_tflops', 0):.2f}\n")
-    else:
-        lines.append("#### GPU Performance: N/A\n")
+    #gpu_perf = analysis_result.get("gpu_performance", {})
+    #if gpu_perf:
+    #    lines.append("#### GPU Performance:")
+    #    for gpu, perf in gpu_perf.items():
+        #    lines.append(f"**{gpu}:**")
+       #     lines.append(f"  - Tokens per Second: {perf.get('tokens_per_second', 0):.2f}")
+      #      lines.append(f"  - FLOPs per Token: {perf.get('flops_per_token', 0):.2f}")
+     #       lines.append(f"  - Effective TFLOPS: {perf.get('effective_tflops', 0):.2f}\n")
+    #else:
+    #    lines.append("#### GPU Performance: N/A\n")
     
     return "\n".join(lines)
 
@@ -203,7 +204,7 @@ elif page == "On Premise Estimator":
     st.divider()
     st.header("On Premise Estimator")
     st.markdown("Enter a Hugging Face model ID to perform an on premise analysis using the provided estimator.")
-    hf_model_id = st.text_input("Hugging Face Model ID", value="facebook/opt-1.3b")
+    hf_model_id = st.text_input("Hugging Face Model ID", value="meta-llama/Llama-4-Scout-17B-16E")
     
     if st.button("Analyze Model"):
         with st.spinner("Analyzing model..."):
@@ -225,7 +226,7 @@ elif page == "About":
         - The app downloads the latest pricing from the LiteLLM repository.
         - Using simple maths to estimate the total tokens.
         - Helps you estimate hardware requirements for running open-source large language models (LLMs) on-premise using only the model ID from Hugging Face.
-        - Version 0.1
+        - Latest Version 0.1
 
         ---
 
@@ -233,8 +234,9 @@ elif page == "About":
 
         | Version | Release Date | Key Feature Updates |
         |--------|--------------|---------------------|
+        | `v1.1` | 2025-04-06  | Added On Premise Estimator Feature |
         | `v1.0` | 2025-03-26  | Initial release with basic total tokens estimation |
-        | `v1.1` | 2025-04-06  | Added On Premise Estimator Tab |
+        
 
         ---
 
@@ -242,9 +244,12 @@ elif page == "About":
         """
     )
     st.markdown(
-        """
-        ### Disclaimer
+    """
+    ### Found a Bug?
+
+    If you encounter any issues or have feedback, please email to **[email protected]**
+
+    Your input helps us improve the app!
+    """
+)
 
-        This app is for demonstration purposes only. Actual costs may vary based on usage patterns and other factors.
-        """
-    )