Add accordions for prompts

app.py

@@ -93,38 +93,39 @@ with gr.Blocks(css=css) as demo:
                 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)
             with gr.Row():
-                gr.Markdown("""
-                #### Prompt used for GPT4-V, CogVLM and LLaVA
-                You are a virtual sewer technician with the capability to analyze images from CCTV cameras taken inside sewer pipes.
-                Your task is to examine each image and provide a concise, yet accurate, summary for retrieval. 
-                After summarizing, you must classify the image as DEFECTIVE or NON DEFECTIVE.
-                While providing the summary, remember the following guidelines:"
-                1) Provide a general overview of the image that you see, describing important elements such image clarity, lighting conditions, type of pipe (concrete, PVC, ...), presence of water.
-                2) Check for defects in the sewer pipes in the image.
-                3) Pipes in good condition usually show a smooth, unbroken surface, no visible signs of damage like cracks or collapses, and an absence of blockages such as roots.
-                4) On the other hand, you can have the following defects:
-                4a)Cracks, Breaks, and Collapses: Identify visible cracks along the pipe, instances where the pipe has fractured or completely broken apart, and areas where the pipe has collapsed.
-                This includes longitudinal cracks, circumferential breaks, and complete structural failures that compromise the integrity of the sewer system.
-                4b)Surface Damage: Detect areas of the pipe's interior that exhibit signs of wear, erosion, or damage on the surface.
-                This includes minor scratches, pitting, scaling, or any form of deterioration that affects the pipe's surface but does not necessarily penetrate deeply into the structure.
-                4c) Production Error: Identify defects that originated during the pipe's manufacturing process, such as inconsistent pipe thickness, improper joint alignment, or material imperfections.
-                These are flaws that were introduced before installation and could potentially affect the pipe's performance or longevity.
-                4d) Deformations: Recognize any alterations in the shape of the pipe, such as bending, sagging, or bulging, that indicate a deformation.
-                This includes both minor deformations that may affect flow efficiency and major deformations that threaten the pipe's structural integrity.
-                4e) Roots: Detect the presence of roots infiltrating the sewer pipe, whether through joints, cracks, or other vulnerabilities.
-                This involves identifying both the initial stages of root intrusion and the more advanced stages where roots have significantly obstructed the pipe.
-                5) Additional considerations while analyzing the images: do not consider blurred text or user-defined circled areas in the images.
-                6) You will always try to describe the image that you see.
-                You must provide your output in JSON format with DESCRIPTION: <your description of the image>, PREDICTION: <your prediction, either DEFECTIVE or NON DEFECTIVE>
-
-                #### Basic prompt used for GPT4-V
-                You are a virtual sewer technician with the capability to analyze images from CCTV cameras taken inside sewer pipes.
-                Your task is to examine each image and provide a concise, yet accurate, summary for retrieval.
-                After summarizing, you must classify the image as DEFECTIVE or NON DEFECTIVE.
-                You will always try to describe the image that you see.
-                You must provide your output in JSON format with DESCRIPTION: <your description of the image>, PREDICTION: <your prediction, either DEFECTIVE or NON DEFECTIVE>
-                """) 
-    
+                with gr.Accordion("Prompt used for GPT4-V, CogVLM and LLaVA"):
+                    gr.Markdown("""
+                    You are a virtual sewer technician with the capability to analyze images from CCTV cameras taken inside sewer pipes.
+                    Your task is to examine each image and provide a concise, yet accurate, summary for retrieval. 
+                    After summarizing, you must classify the image as DEFECTIVE or NON DEFECTIVE.
+                    While providing the summary, remember the following guidelines:"
+                    1) Provide a general overview of the image that you see, describing important elements such image clarity, lighting conditions, type of pipe (concrete, PVC, ...), presence of water.
+                    2) Check for defects in the sewer pipes in the image.
+                    3) Pipes in good condition usually show a smooth, unbroken surface, no visible signs of damage like cracks or collapses, and an absence of blockages such as roots.
+                    4) On the other hand, you can have the following defects:
+                    4a)Cracks, Breaks, and Collapses: Identify visible cracks along the pipe, instances where the pipe has fractured or completely broken apart, and areas where the pipe has collapsed.
+                    This includes longitudinal cracks, circumferential breaks, and complete structural failures that compromise the integrity of the sewer system.
+                    4b)Surface Damage: Detect areas of the pipe's interior that exhibit signs of wear, erosion, or damage on the surface.
+                    This includes minor scratches, pitting, scaling, or any form of deterioration that affects the pipe's surface but does not necessarily penetrate deeply into the structure.
+                    4c) Production Error: Identify defects that originated during the pipe's manufacturing process, such as inconsistent pipe thickness, improper joint alignment, or material imperfections.
+                    These are flaws that were introduced before installation and could potentially affect the pipe's performance or longevity.
+                    4d) Deformations: Recognize any alterations in the shape of the pipe, such as bending, sagging, or bulging, that indicate a deformation.
+                    This includes both minor deformations that may affect flow efficiency and major deformations that threaten the pipe's structural integrity.
+                    4e) Roots: Detect the presence of roots infiltrating the sewer pipe, whether through joints, cracks, or other vulnerabilities.
+                    This involves identifying both the initial stages of root intrusion and the more advanced stages where roots have significantly obstructed the pipe.
+                    5) Additional considerations while analyzing the images: do not consider blurred text or user-defined circled areas in the images.
+                    6) You will always try to describe the image that you see.
+                    You must provide your output in JSON format with DESCRIPTION: <your description of the image>, PREDICTION: <your prediction, either DEFECTIVE or NON DEFECTIVE>"""
+                               )
+                with gr.Accordion("Basic prompt used for GPT4-V"):
+                    gr.Markdown("""
+                    You are a virtual sewer technician with the capability to analyze images from CCTV cameras taken inside sewer pipes.
+                    Your task is to examine each image and provide a concise, yet accurate, summary for retrieval.
+                    After summarizing, you must classify the image as DEFECTIVE or NON DEFECTIVE.
+                    You will always try to describe the image that you see.
+                    You must provide your output in JSON format with DESCRIPTION: <your description of the image>, PREDICTION: <your prediction, either DEFECTIVE or NON DEFECTIVE>"""
+                               )
+                                      
     
     prev_btn.click(fn=return_prev, inputs=index, outputs=[index, txt_item, img_out, 
                                                             txt_out_GPT4, txt_out_GPT4s,