Update app.py

app.py

@@ -1,17 +1,44 @@
 import gradio as gr
 import json
 from graphviz import Digraph
-import base64
+import os
+from tempfile import NamedTemporaryFile
 
 def generate_concept_map(json_input: str) -> str:
     """
-    Generate concept map from JSON and return as base64 image
+    Generate concept map from JSON and return as image file
     
     Args:
         json_input (str): JSON describing the concept map structure.
         
+            REQUIRED FORMAT EXAMPLE:
+            {
+                "central_node": "AI",
+                "nodes": [
+                    {
+                        "id": "ml",
+                        "label": "Machine Learning",
+                        "relationship": "subcategory",
+                        "subnodes": [
+                            {
+                                "id": "dl",
+                                "label": "Deep Learning",
+                                "relationship": "type",
+                                "subnodes": [
+                                    {
+                                        "id": "cnn",
+                                        "label": "CNN",
+                                        "relationship": "architecture"
+                                    }
+                                ]
+                            }
+                        ]
+                    }
+                ]
+            }
+    
     Returns:
-        str: Base64 data URL of the generated concept map
+        str: Path to generated PNG image file
     """
     try:
         if not json_input.strip():
@@ -101,10 +128,10 @@ def generate_concept_map(json_input: str) -> str:
                     fontsize='8'
                 )
 
-        # Convert to base64 image
-        img_data = dot.pipe(format='png')
-        img_base64 = base64.b64encode(img_data).decode()
-        return f"data:image/png;base64,{img_base64}"
+        # Save to temporary file
+        with NamedTemporaryFile(delete=False, suffix='.png') as tmp:
+            dot.render(tmp.name, format='png', cleanup=True)
+            return tmp.name + '.png'
 
     except json.JSONDecodeError:
         return "Error: Invalid JSON format"
@@ -112,7 +139,7 @@ def generate_concept_map(json_input: str) -> str:
         return f"Error: {str(e)}"
 
 if __name__ == "__main__":
-    # Sample JSON for placeholder
+    # Complex sample JSON
     sample_json = """
     {
       "central_node": "Artificial Intelligence (AI)",
@@ -120,65 +147,60 @@ if __name__ == "__main__":
         {
           "id": "ml",
           "label": "Machine Learning",
-          "relationship": "core_component",
+          "relationship": "Core Component",
           "subnodes": [
             {
               "id": "sl",
               "label": "Supervised Learning",
-              "relationship": "type_of",
+              "relationship": "Learning Type",
               "subnodes": [
-                {"id": "reg", "label": "Regression", "relationship": "technique"},
-                {"id": "clf", "label": "Classification", "relationship": "technique"}
+                {
+                  "id": "reg",
+                  "label": "Regression",
+                  "relationship": "Technique",
+                  "subnodes": [
+                    {"id": "lr", "label": "Linear Regression", "relationship": "Algorithm"}
+                  ]
+                },
+                {
+                  "id": "clf",
+                  "label": "Classification",
+                  "relationship": "Technique",
+                  "subnodes": [
+                    {"id": "svm", "label": "SVM", "relationship": "Algorithm"},
+                    {"id": "rf", "label": "Random Forest", "relationship": "Algorithm"}
+                  ]
+                }
               ]
             },
             {
               "id": "ul",
               "label": "Unsupervised Learning",
-              "relationship": "type_of",
+              "relationship": "Learning Type",
               "subnodes": [
-                {"id": "clus", "label": "Clustering", "relationship": "technique"},
-                {"id": "dim", "label": "Dimensionality Reduction", "relationship": "technique"}
+                {
+                  "id": "clus",
+                  "label": "Clustering",
+                  "relationship": "Technique",
+                  "subnodes": [
+                    {"id": "kmeans", "label": "K-Means", "relationship": "Algorithm"}
+                  ]
+                }
               ]
             }
           ]
         },
         {
           "id": "nlp",
-          "label": "Natural Language Processing",
-          "relationship": "application_area",
+          "label": "NLP",
+          "relationship": "Application Domain",
           "subnodes": [
             {
               "id": "sa",
               "label": "Sentiment Analysis",
-              "relationship": "task",
-              "subnodes": [
-                {"id": "tc", "label": "Text Classification", "relationship": "method"},
-                {"id": "absa", "label": "Aspect-Based Sentiment Analysis", "relationship": "method"}
-              ]
-            },
-            {
-              "id": "tr",
-              "label": "Translation",
-              "relationship": "task",
-              "subnodes": [
-                {"id": "nmt", "label": "Neural Machine Translation", "relationship": "method"},
-                {"id": "rbt", "label": "Rule-Based Translation", "relationship": "method"}
-              ]
-            }
-          ]
-        },
-        {
-          "id": "cv",
-          "label": "Computer Vision",
-          "relationship": "application_area",
-          "subnodes": [
-            {
-              "id": "od",
-              "label": "Object Detection",
-              "relationship": "task",
+              "relationship": "Task",
               "subnodes": [
-                {"id": "yolo", "label": "YOLO", "relationship": "algorithm"},
-                {"id": "rcnn", "label": "R-CNN", "relationship": "algorithm"}
+                {"id": "tb", "label": "Transformer-Based", "relationship": "Approach"}
               ]
             }
           ]
@@ -190,18 +212,18 @@ if __name__ == "__main__":
     demo = gr.Interface(
         fn=generate_concept_map,
         inputs=gr.Textbox(
-            value=sample_json,  # Pre-filled sample JSON
-            placeholder="Paste structured JSON here...",
-            label="JSON Input",
-            lines=15
+            value=sample_json,
+            placeholder="Paste JSON following the documented format",
+            label="Structured JSON Input",
+            lines=25
         ),
         outputs=gr.Image(
-            label="Concept Map",
+            label="Generated Concept Map",
             type="filepath",
-            interactive=False
+            show_download_button=True
         ),
         title="Advanced Concept Map Generator",
-        description="Create complex concept maps from JSON with direct image output"
+        description="Create multi-level concept maps from properly formatted JSON"
     )
     
     demo.launch(