Update app.py

app.py

@@ -40,7 +40,7 @@ def answer_questions(image_tuples, prompt_text):
         for j, image_tuple in enumerate(image_tuples):
             image_name = f"image{j+1}"
             answer_text = answers[i][j]  # Retrieve the answer for the i-th prompt for the j-th image
-            Q_and_A += f"**{image_name} A:** \n *{answer_text}* \n\n"
+            Q_and_A += f"**{image_name} A:** \n {answer_text} \n\n"
 
     result = {'headers': prompts, 'data': answers}  # Updated result handling
     print(f"result\n{result}\n\nQ_and_A\n{Q_and_A}\n\n")
@@ -53,13 +53,15 @@ with gr.Blocks() as demo:
     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", preview=True, object_fit="scale-down")
+        img = gr.Gallery(label="Upload Images", type="pil", preview=True, columns=4)
     with gr.Row():
         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.Markdown(label="Questions and Answers", line_breaks=True)
-    output2 = gr.Dataframe(label="Structured Dataframe", type="array", wrap=True)
+    with gr.Row():
+        output = gr.Markdown(label="Questions and Answers", line_breaks=True)
+    with gr.Row():
+        output2 = gr.Dataframe(label="Structured Dataframe", type="array", wrap=True)
     submit.click(answer_questions, [img, prompt], [output, output2])
 
 demo.queue().launch()