Added batch operations

app.py

@@ -7,7 +7,7 @@ import tempfile
 import os
 import os.path
 
-BLIP_MODEL_ID = "Salesforce/blip2-opt-2.7b"
+BLIP_MODEL_ID = "Salesforce/blip2-opt-6.7b"
 CAPTION_CSV_DIR=os.path.join(os.getcwd(), 'csv')
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@@ -19,37 +19,45 @@ if device.type != 'cuda':
 blip_processor = Blip2Processor.from_pretrained(BLIP_MODEL_ID)
 blip_model = Blip2ForConditionalGeneration.from_pretrained(BLIP_MODEL_ID, torch_dtype=torch.float16, device_map="auto").to(device)
 
-def captions_images_to_csv(images: list[PIL.Image.Image]) -> str:
-    caption_map = caption_images(images)
+def captions_images_to_csv(images: list[PIL.Image.Image], prompt: str) -> str:
+    caption_map = caption_images(images, prompt)
     lines = [k + "," + v + "\n" for k, v in caption_map.items()]
     
     with tempfile.NamedTemporaryFile('w', dir=CAPTION_CSV_DIR, delete=False, suffix=".csv") as f:    
         f.writelines(lines)
-        return f.name
+        return [f.name, ''.join(lines)]
     
     
-def caption_images(images: list[PIL.Image.Image]) -> dict[str, str]:
-    image_files = [PIL.Image.open(image.name).convert('RGB') for image in images]
-    inputs = blip_processor(images=image_files, return_tensors='pt').to(device, torch.float16)
+def caption_images(images: list[PIL.Image.Image], prompt: str) -> dict[str, str]:   
+    results = {}
     
-    for image in image_files:
-        image.close()
-    
-    generated_ids = blip_model.generate(**inputs, max_new_tokens=20)
-    
-    results = blip_processor.batch_decode(generated_ids, skip_special_tokens=True)
-    
-    return dict(zip([Path(image.name).stem for image in images], [result.replace("\n", "") for result in results]))
+    for image in images:
+        try:
+            with PIL.Image.open(image.name).convert('RGB') as i:
+                inputs = blip_processor(i, text=prompt, return_tensors='pt').to(device, torch.float16)
+                generated_ids = blip_model.generate(**inputs, max_new_tokens=20)
+                results[Path(image.name).stem] = blip_processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+                
+        except Exception as e: 
+            print("An error occured while trying to process image: " + image.name)
+            print(str(e))
+
+    return results
 
 
 with gr.Blocks() as demo:
     with gr.Tab("Image Caption Bot"):
-        images_input = gr.File(file_count="multiple", file_types=["image"])
+        images_input = gr.File(label= "Input Images", file_count="multiple", file_types=["image"])
+        caption_prompt = gr.Textbox(label="Prompt")
         caption_images_button = gr.Button("Submit")
-        image_caption_output = gr.File()
+        image_caption_output_file = gr.File(label="Output CSV")
+        image_caption_output_label = gr.Textbox(label="Output Data", interactive=False)
         
 
-    caption_images_button.click(captions_images_to_csv, inputs=[images_input], outputs=image_caption_output)
+    caption_images_button.click(
+        captions_images_to_csv, 
+        inputs=[images_input, caption_prompt], 
+        outputs=[image_caption_output_file, image_caption_output_label])
 
 
 print("Launching Gradio")