Update app.py

app.py

@@ -56,7 +56,7 @@ def predict(text, num_nodes = None):
     avg_deg = np.mean(degs)
     avg_cc = np.mean(ccs)
 
-    return images[0], images[1], images[2], images[3], images[4], ccs[0], ccs[1], ccs[2], ccs[3], ccs[4], degs[0], degs[1], degs[2], degs[3], degs[4], avg_cc, avg_deg
+    return images[0], images[1], images[2], ccs[0], ccs[1], ccs[2], degs[0], degs[1], degs[2], avg_cc, avg_deg
 
 def clear(input_text):
     return None, None
@@ -68,17 +68,17 @@ with gr.Blocks() as demo:
         with gr.Column():
             input_text = gr.Textbox(label="Input your text prompt here", placeholder="Type here...")
         with gr.Column():
-            input_num = gr.Slider(5, 200, value=10, label="Count", info="Number of nodes in the graph to be generated")
+            input_num = gr.Slider(1, 200, value=10, label="Count", info="Number of nodes in the graph to be generated")
         with gr.Column():
             gr.Markdown("### Suggested Prompts")
             gr.Markdown("1. Create a complex network with high clustering coefficient.\n2. Create a graph with extremely low number of triangles.\n 3. Please give me a Power Network with extremely low number of triangles but with medium level of average degree.")
 
     with gr.Row() as output_row:
-        output_images = [gr.Image(label = f"Generated Network #{_}") for _ in range(5)]
+        output_images = [gr.Image(label = f"Generated Network #{_}") for _ in range(3)]
     with gr.Row():
-        output_texts_cc = [gr.Textbox(label=f"CC #{_}") for _ in range(5)]
+        output_texts_cc = [gr.Textbox(label=f"CC #{_}") for _ in range(3)]
     with gr.Row():
-        output_texts_deg = [gr.Textbox(label=f"DEG #{_}") for _ in range(5)]
+        output_texts_deg = [gr.Textbox(label=f"DEG #{_}") for _ in range(3)]
     
     with gr.Row():
         avg_cc_text = gr.Textbox(label="Average Clustering Coefficient")