image correct

app.py

@@ -275,14 +275,14 @@ Customer: "No, thank you."
     )
     btn_recommend = gr.Button("Graph It!")
     btn_clear = gr.ClearButton(components=[out_product])
-    btn_recommend.click(fn=graph, inputs=in_verbatim, outputs=out_product)
+    btn_recommend.click(fn=graph, inputs=[in_verbatim, out_product], outputs=out_product)
 
 
     gr.Markdown("""
 Example of Customer Profile in Graph
 =================
-![Customer Needs and Pain Points](./knowledge_graph1.png)
-![Accumulated Interaction for the same Customer Needs and Pain Points](./knowledge_graph2.png)
+![Customer Needs and Pain Points](/file=knowledge_graph1.png)
+![Accumulated Interaction for the same Customer Needs and Pain Points](/file=knowledge_graph2.png)
 
 Benefits of a Knowledge Graph
 ============
@@ -447,7 +447,7 @@ Customer: "No, thank you."
     gr.Markdown("""
 Example of Call Resolution
 ===============
-![Resolution for Clear Picture about Customer Issue](./entity.png)
+![Resolution for Clear Picture about Customer Issue](/file=entity.png)
 
 Companies like RBC, Comcast, or BMO often face a recurring challenge: long, complex customer service calls filled with vague product references, overlapping account details, and unstructured issue descriptions. This makes it difficult for support teams and analytics engines to extract clear insights or resolve recurring pain points across accounts and products.
 

knowledge.py

@@ -155,7 +155,7 @@ def expandIssue(input) -> Iterable[Subissue]:
 
 def graph(query, queryx):
   #queryx = expandIssue(query)
-  if queryx is None:
+  if queryx.strip() == "":
     graph = generate_graph(query)
   else:
     graph = generate_graph(query, KnowledgeGraph.model_validate_json(queryx))