explain

app.py

@@ -225,7 +225,7 @@ With no need for jargon, SSDS delivers tangible value to our fintech operations.
     gr.Markdown("""
     Explain by Dataset
     =============
-    ![summary](("/file=./xgb/data.png"))
+    ![summary](("/file=data.png"))
     
     sorted feature from top(most import)
     
@@ -240,7 +240,7 @@ With no need for jargon, SSDS delivers tangible value to our fintech operations.
     
     Explain by Feature
     =============
-    ![partial_dependence](("/file=./xgb/feature.png"))
+    ![partial_dependence](("/file=feature.png"))
 
     dist lower than 900 spike the price f(x)
     
@@ -248,7 +248,7 @@ With no need for jargon, SSDS delivers tangible value to our fintech operations.
     
     Explain by Record
     =============
-    ![force](("/file=./xgb/record.png"))
+    ![force](("/file=record.png"))
     
     the largest contribution to positive price is dist_subway
     
@@ -256,11 +256,13 @@ With no need for jargon, SSDS delivers tangible value to our fintech operations.
 
     Explain by Instance
     =============
-    ![dependence](("/file=./xgb/instance.png"))
+    ![dependence](("/file=instance.png"))
     
     at around 500 dist_subway, it possible for positive impact and negative impact for price
     
     over all trend is negative that mean, closer to subway is contribute to higher price
+    
+    there is a point at 6500 far from subway and it has negative impact on price, despite is is close to store(dist_stores)
     """)