update app.py

.python-version

@@ -0,0 +1 @@
+3.11

app.py

@@ -45,16 +45,32 @@ class GradioUIWithExamples(GradioUI):
         super().__init__(agent, **kwargs)
         self.examples = examples
 
+
     def build_interface(self):
         with gr.Blocks() as demo:
-            gr.Markdown("## Biomedical Answers")
-            gr.Markdown("""
-            An experimental knowledge platform that attempts to transforms fragmented biomedical data into answers.
+            # Title Section
+            gr.Markdown("## From Answers to Insight - Architecting Evolvable Agentic Systems")
+
+            # Description Section
+            gr.Markdown(
+                """
+                **🔬 Architectural Blueprint**: *Production systems require intentional design - combining smolagents' efficiency with Phoenix observability, powered by ontology-driven prompting patterns.*
+                """
+            )
 
-            A word of caution... this is a journey of discovery to understand what the Smolagents coding approach can provide with a simple app and a handful of dependencies.  In terms of the responses provided it has a ways to go until I would claim that it provides true knowledge or insight.
+            # Core Innovation Vectors Section
+            with gr.Accordion("Core Innovation Vectors", open=True):
+                gr.Markdown(
+                    """
+                    - ✅ **Competency Question Engine**: Structured prompting using domain vocabulary
+                    - ✅ **Telemetry-First Core**: Phoenix spans capturing full evidence chains, establishing OpenTelemetry metrics pipeline for performance benchmarking
+                    - ✅ **Domain Foundation**: Smolagents + biomedical vocabulary layer
+                    - 🛠️ **Human-in-the-Loop Evolution**: Gradio UI with human feedback capture
+                    - 🛠️ **Benchmarking Rigor**: Validation scorecards and audit framework
+                    - 🛠️ **Domain Insight Engine**: Knowledge graph grounding and multimodal evidence synthesis
+                    """
+                )
 
-            If anything I think that it highlights the value of ontologies and associated vocabularies in guiding LLM behavior.  Probably a good time to go back and review some notes on DSPy and layer on domain vocabularies...
-            """)
 
             input_box = gr.Textbox(
                 label="Your Question",
@@ -91,7 +107,7 @@ model = HfApiModel(model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', token=HF
 agent = CodeAgent(
     tools=[],
     model=model,
-    additional_authorized_imports=["gradio","pandas","numpy","datasets","duckdb","json","streamlit"],
+    additional_authorized_imports=["gradio","pandas","numpy","datasets","duckdb","json","streamlit","requests","json"],
     add_base_tools=True
 )