Update app.py

app.py

@@ -4,40 +4,45 @@ import torch
 from PIL import Image
 from transformers import AutoProcessor, AutoModelForCausalLM
 
-
-
 subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
 florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
 
-def generate_caption(image):
+def generate_captions(image):
     if not isinstance(image, Image.Image):
         image = Image.fromarray(image)
     
     inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
-    generated_ids = florence_model.generate(
-        input_ids=inputs["input_ids"],
-        pixel_values=inputs["pixel_values"],
-        max_new_tokens=1024,
-        early_stopping=False,
-        do_sample=False,
-        num_beams=3,
-    )
-    generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-    parsed_answer = florence_processor.post_process_generation(
-        generated_text,
-        task="<MORE_DETAILED_CAPTION>",
-        image_size=(image.width, image.height)
-    )
-    prompt =  parsed_answer["<MORE_DETAILED_CAPTION>"]
-    print("\n\nGeneration completed!:"+ prompt)
-    return prompt
     
-io = gr.Interface(generate_caption,
-                  inputs=[gr.Image(label="Input Image")],
-                  outputs = [gr.Textbox(label="Output Prompt", lines=2, show_copy_button = True),
-                            ]
-                 )
+    captions = []
+    for i in range(3):
+        generated_ids = florence_model.generate(
+            input_ids=inputs["input_ids"],
+            pixel_values=inputs["pixel_values"],
+            max_new_tokens=1024,
+            early_stopping=False,
+            do_sample=True,  
+            temperature=0.7 + i * 0.1,
+            num_beams=3
+        )
+        generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+        parsed_answer = florence_processor.post_process_generation(
+            generated_text,
+            task="<MORE_DETAILED_CAPTION>",
+            image_size=(image.width, image.height)
+        )
+        prompt = parsed_answer["<MORE_DETAILED_CAPTION>"]
+        captions.append(prompt)
+        print(f"\n\nGeneration {i+1} completed!:" + prompt)
+    
+    return "\n\n".join([f"Caption {i+1}: {caption}" for i, caption in enumerate(captions)])
+
+io = gr.Interface(
+    generate_captions,
+    inputs=[gr.Image(label="Input Image")],
+    outputs=[gr.Textbox(label="Output Captions", lines=10, show_copy_button=True)]
+)
+
 io.launch(debug=True)