v4

app.py

@@ -43,7 +43,7 @@ class ModelPredictor:
             "YODPPROB": ["No other problems for 2+ weeks",         "Had other problems for 2+ weeks"],
             "YOWRPROB": ["No 'worst time ever' feeling",           "Had 'worst time ever' feeling"],
             "YODPR2WK": ["No depressed feelings for 2+ wks",       "Had depressed feelings for 2+ wks"],
-            "YOWRDEPR": ["Did NOT feel sad/depressed daily",        "Felt sad/depressed mostly everyday"],
+            "YOWRDEPR": ["Did NOT feel sad/depressed daily",       "Felt sad/depressed mostly everyday"],
             "YODPDISC": ["Overall mood not sad/depressed",         "Overall mood was sad/depressed"],
             "YOLOSEV":  ["Did NOT lose interest in things",        "Lost interest in enjoyable things"],
             "YOWRDCSN": ["Was able to make decisions",             "Was unable to make decisions"],
@@ -425,37 +425,57 @@ def combined_plot(feature_list, label_col):
     If user picks 1 feature => distribution plot.
     If user picks 2 features => co-occurrence plot.
     Otherwise => show error or empty plot.
+
+    This function also maps numeric codes to text using 'input_mapping'
+    and 'predictor.prediction_map' so that the plots display more readable labels.
     """
     if not label_col:
         return px.bar(title="Please select a label column.")
 
+    # Make a copy of your dataset
+    df_copy = df.copy()
+
+    # A) Convert numeric codes -> text for each feature in `input_mapping`
+    for col, text_to_num_dict in input_mapping.items():
+        if col in df_copy.columns:
+            # Reverse mapping: "Yes"->1 becomes 1->"Yes"
+            num_to_text = {v: k for k, v in text_to_num_dict.items()}
+            df_copy[col] = df_copy[col].map(num_to_text).fillna(df_copy[col])
+
+    # B) Convert label 0/1 to text in df_copy if label_col is in predictor.prediction_map
+    if label_col in predictor.prediction_map and label_col in df_copy.columns:
+        zero_text, one_text = predictor.prediction_map[label_col]
+        label_map = {0: zero_text, 1: one_text}
+        df_copy[label_col] = df_copy[label_col].map(label_map).fillna(df_copy[label_col])
+
+    # Now proceed with the plotting
     if len(feature_list) == 1:
         f_ = feature_list[0]
-        if f_ not in df.columns or label_col not in df.columns:
+        if f_ not in df_copy.columns or label_col not in df_copy.columns:
             return px.bar(title="Selected columns not found in the dataset.")
-        grouped = df.groupby([f_, label_col]).size().reset_index(name="count")
+        grouped = df_copy.groupby([f_, label_col]).size().reset_index(name="count")
         fig = px.bar(
-            grouped, 
-            x=f_, 
-            y="count", 
+            grouped,
+            x=f_,
+            y="count",
             color=label_col,
-            title=f"Distribution of {f_} vs {label_col}"
+            title=f"Distribution of {f_} vs {label_col} (Text Mapped)"
         )
         fig.update_layout(width=1200, height=600)
         return fig
 
     elif len(feature_list) == 2:
         f1, f2 = feature_list
-        if (f1 not in df.columns) or (f2 not in df.columns) or (label_col not in df.columns):
+        if (f1 not in df_copy.columns) or (f2 not in df_copy.columns) or (label_col not in df_copy.columns):
             return px.bar(title="Selected columns not found in the dataset.")
-        grouped = df.groupby([f1, f2, label_col]).size().reset_index(name="count")
+        grouped = df_copy.groupby([f1, f2, label_col]).size().reset_index(name="count")
         fig = px.bar(
-            grouped, 
-            x=f1, 
-            y="count", 
+            grouped,
+            x=f1,
+            y="count",
             color=label_col,
-            facet_col=f2, 
-            title=f"Co-occurrence: {f1}, {f2} vs {label_col}"
+            facet_col=f2,
+            title=f"Co-occurrence: {f1}, {f2} vs {label_col} (Text Mapped)"
         )
         fig.update_layout(width=1200, height=600)
         return fig
@@ -579,11 +599,12 @@ with gr.Blocks(css=".gradio-container {max-width: 1200px;}") as demo:
     # ======== TAB 2: Unified Distribution/Co-occurrence ========
     with gr.Tab("Distribution/Co-occurrence"):
         gr.Markdown("### Select 1 or 2 features + 1 label to see a bar chart.")
-    
-        # Instead of using df.columns, we now only use the keys from input_mapping
+
+        # Show only your 25 input features
         list_of_features = sorted(input_mapping.keys())
+        # Show all label columns from the predictor map
         list_of_labels = sorted(predictor.prediction_map.keys())
-    
+
         selected_features = gr.CheckboxGroup(
             choices=list_of_features,
             label="Select 1 or 2 features"
@@ -592,10 +613,10 @@ with gr.Blocks(css=".gradio-container {max-width: 1200px;}") as demo:
             choices=list_of_labels,
             label="Label Column (e.g., YOWRCONC, YOSEEDOC, etc.)"
         )
-    
+
         generate_combined_btn = gr.Button("Generate Plot")
         combined_output = gr.Plot()
-    
+
         generate_combined_btn.click(
             fn=combined_plot,
             inputs=[selected_features, label_dd],