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app.py

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+import pandas as pd
+import gradio as gr
+import numpy as np
+
+df_table_dsc = pd.read_csv('./results/df_table_dsc.csv')
+df_table_prd = pd.read_csv('./results/df_table_prd.csv')
+n_lines = 12
+
+defect_dict = {'NoDefect':'No defect', 'RO': 'Defect: Roots', 'OB': 'Defect: Surface damage', 
+               'RB': 'Defect: Cracks, breaks, and collapses', 'PF': 'Defect: Production error', 
+               'DE':'Defect: Deformation'}
+
+
+css="""
+#image-out {
+  height: 400px;
+  width: 400px;
+}
+"""
+
+def conv_int(txt):
+    try:
+        return (int(txt))
+    except:
+        return 'NaN'
+
+def return_prev(index):
+    # Update current index based on navigation input
+    index -=1 
+    # Ensure index is within bounds
+    index = max(0, min(index, len(df_table_dsc) - 1))
+
+    image_path, gpt4, gpt4b, cogvlm, llava, defect_class, predictions = return_image(index)
+    return index, index, image_path, gpt4, gpt4b, cogvlm, llava, defect_class, predictions
+
+def return_next(index):
+    # Update current index based on navigation input
+    index +=1 
+    # Ensure index is within bounds
+    index = max(0, min(index, len(df_table_dsc) - 1))
+    image_path, gpt4, gpt4b, cogvlm, llava, defect_class, predictions = return_image(index)
+    return index, index, image_path, gpt4, gpt4b, cogvlm, llava, defect_class, predictions
+
+def return_image(index):
+    # Fetch row from DataFrame
+    row = df_table_dsc.iloc[index]
+    image_path = f"./images/{row['img_id']}"
+
+    defect_class =  defect_dict[row['defect_type']]    
+    
+    row_prd = df_table_prd.iloc[index]
+    
+    predictions = f"XIE:{conv_int(row_prd['Xie']>0.5)}, GPT4:{conv_int(row_prd['GPT4'])}, GPT4s:{conv_int(row_prd['GPT4_basic'])}, CogVLM:{conv_int(row_prd['CogVLM'])}, LLaVA:{conv_int(row_prd['LLaVA'])}"
+
+    # Return iamge path and descriptions
+    return image_path, row['GPT4'], row['GPT4_basic'], row['CogVLM'], row['LLaVA'], defect_class, predictions
+
+
+with gr.Blocks(css=css) as demo: 
+    # gr.Markdown("""
+                ## Demo for 'The Potential of Generative AI for the Urban Water Sector'
+                ### Riccardo Taormina, Delft University of Technology (TU Delft), Department of Water Management                
+                ### email: r.taormina@tudelft.nl""")                
+    gr.Markdown("""
+                ## Testing Large Multimodal Models for Sewer Defect Inspection 
+                Press on \<Next\> or \<Previous\> to start!
+                """)                
+    index = gr.Number(value=-1, visible=False)      
+    with gr.Row():
+        with gr.Column(scale=1):
+            img_out = gr.Image(type="filepath", label="Image", elem_id="image-out")
+            with gr.Row():
+                txt_item = gr.Textbox(label="Sample no.", min_width= 20)
+                txt_defect_class = gr.Textbox(label="Defect class", interactive=False, min_width= 150)
+            txt_xie_pred = gr.Textbox(label="Predictions (XIE = benchmark, 0 = No Defect, 1 = Defect)", min_width= 300, interactive=False)
+            with gr.Row():
+                prev_btn = gr.Button("Previous")            
+                next_btn = gr.Button("Next")            
+        with gr.Column(scale=1):
+            gr.Markdown('Multimodal descriptions')
+            with gr.Row():
+                txt_out_GPT4 = gr.Textbox(label="GPT4", lines= n_lines, max_lines=n_lines)
+                txt_out_GPT4s = gr.Textbox(label="GPT4 simple", lines= n_lines, max_lines=n_lines)    
+            with gr.Row():
+                txt_out_CogVLM = gr.Textbox(label="CogVLM", lines= n_lines, max_lines=n_lines)
+                txt_out_LLaVa = gr.Textbox(label="LLaVa", lines= n_lines, max_lines=n_lines)
+    prev_btn.click(fn=return_prev, inputs=index, outputs=[index, txt_item, img_out, 
+                                                            txt_out_GPT4, txt_out_GPT4s, 
+                                                            txt_out_CogVLM, txt_out_LLaVa, txt_defect_class, txt_xie_pred])
+    next_btn.click(fn=return_next, inputs=index, outputs=[index, txt_item, img_out, 
+                                                            txt_out_GPT4, txt_out_GPT4s, 
+                                                            txt_out_CogVLM, txt_out_LLaVa, txt_defect_class, txt_xie_pred])
+
+if __name__ == "__main__":
+    demo.launch()