Update app.py

app.py

@@ -38,14 +38,18 @@ def answer_questions(image_tuples, prompt_text):
             tokenizer=tokenizer,
         )
         answers.append(image_answers)
-
+            
     data = []
     for i in range(len(image_tuples)):
         image_name = f"image{i+1}"
         image_answers = [answer[i] for answer in answers]
         print(f"image{i+1}_answers \n {image_answers} \n")
         data.append([image_name] + image_answers)
-
+        
+    for question, answer in zip(prompts, answers):
+        Q_and_A += (f"Q: {question}\nA: {answer}\n\n")
+    print(f"\n\n{Q_and_A}\n\n")
+    
     result = {'headers': prompts, 'data': data}
     return result
     '''
@@ -63,8 +67,9 @@ def answer_questions(image_tuples, prompt_text):
 
 with gr.Blocks() as demo:
     gr.Markdown("# moondream2 unofficial batch processing demo")
-    gr.Markdown("1. Select images\n2. Enter prompts (one prompt for each image provided) separated by commas. Ex: Describe this image, What is in this image?\n\n")
-    gr.Markdown("*Tested and Running on free CPU space tier currently so results may take a bit to process compared to using GPU space hardware*")
+    gr.Markdown("1. Select images\n2. Enter one or more prompts separated by commas. Ex: Describe this image, What is in this image?\n\n")
+    gr.Markdown("**Currently each image will be sent as a batch with the prompts thus asking each promp on each image**")
+    gr.Markdown("*Running on free CPU space tier currently so results may take a bit to process compared to duplicating space and using GPU space hardware*")
     gr.Markdown("## 🌔 moondream2\nA tiny vision language model. [GitHub](https://github.com/vikhyatk/moondream)")
     with gr.Row():
         img = gr.Gallery(label="Upload Images", type="pil")
@@ -72,8 +77,8 @@ with gr.Blocks() as demo:
         prompt = gr.Textbox(label="Input Prompts", placeholder="Enter prompts (one prompt for each image provided) separated by commas. Ex: Describe this image, What is in this image?", lines=8)
     with gr.Row():
         submit = gr.Button("Submit")
-    #output = gr.TextArea(label="Responses", lines=30)
-    output = gr.Dataframw(type="array",wrap=True)
-    submit.click(answer_questions, [img, prompt], output)
+    output = gr.TextArea(label="Questions and Answers", lines=30)
+    output2 = gr.Dataframe(label="Structured Dataframe", type="array",wrap=True)
+    submit.click(answer_questions, [img, prompt], output, output2)
 
 demo.queue().launch()