pit

app2.py

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+import os
+os.environ.setdefault("GRADIO_ANALYTICS_ENABLED", "False")
+import logging
+import gradio as gr
+from pillow_heif import register_heif_opener
+
+register_heif_opener()
+
+from transformers import pipeline
+
+
+LOG_LEVEL = os.getenv("LOG_LEVEL", "DEBUG")
+MAX_NEW_TOKENS = int(os.getenv("MAX_NEW_TOKENS", 200))
+# https://huggingface.co/models?pipeline_tag=image-to-text&sort=likes
+MODEL = os.getenv("MODEL", "Salesforce/blip-image-captioning-large")
+
+logging.basicConfig(level=LOG_LEVEL)
+logger = logging.getLogger(__name__)
+
+
+logger.info("Loading model...")
+# simpler model: "ydshieh/vit-gpt2-coco-en"
+captioner = pipeline(
+  "image-to-text",
+  model=MODEL,
+  max_new_tokens=MAX_NEW_TOKENS,
+)
+logger.info("Done loading model.")
+
+
+
+def graptioner(img):
+  result = captioner(img) 
+  caption = result[0]["generated_text"]
+  return caption
+
+iface = gr.Interface(
+  fn=graptioner,
+  inputs=gr.components.Image(type="pil"),
+  outputs=["text"],
+  allow_flagging="never",
+  # analytics_enabled=False
+)
+
+iface.launch()

app3.py

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+import argparse
+import io
+import logging
+import os
+
+import gradio as gr
+import requests
+from PIL import Image
+from pillow_heif import register_heif_opener
+from transformers import pipeline
+
+os.environ.setdefault("GRADIO_ANALYTICS_ENABLED", "False")
+LOG_LEVEL = os.getenv("LOG_LEVEL", "DEBUG")
+MAX_NEW_TOKENS = int(os.getenv("MAX_NEW_TOKENS", 200))
+# https://huggingface.co/models?pipeline_tag=image-to-text&sort=likes
+MODEL = os.getenv("MODEL", "Salesforce/blip-image-captioning-large")
+
+register_heif_opener()
+
+logging.basicConfig(level=LOG_LEVEL)
+logger = logging.getLogger(__name__)
+
+
+def setup_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--share", action="store_true", default=False)
+    return parser.parse_args()
+
+
+def load_image_from_url(url):
+    try:
+        response = requests.get(url)
+        if not response.ok:
+            raise Exception("Error downloading image")
+        image = Image.open(io.BytesIO(response.content))
+        return image
+    except Exception as e:
+        logger.error("Error loading image from URL: %s", e)
+        raise
+
+
+def graptioner(image, url):
+    if url and url.strip():
+        image = load_image_from_url(url)
+    width, height = image.size
+    if width < 1 or height < 1:
+        raise Exception("Invalid image")
+    logger.debug("Loaded image size: %sx%s", width, height)
+    # generate caption
+    result = captioner(image)
+    return result[0]["generated_text"]
+
+
+if __name__ == "__main__":
+    args = setup_args()
+    logger.info("Loading model...")
+    # simpler model: "ydshieh/vit-gpt2-coco-en"
+    captioner = pipeline(
+        "image-to-text",
+        model=MODEL,
+        max_new_tokens=MAX_NEW_TOKENS,
+    )
+    logger.info("Done loading model.")
+    iface = gr.Interface(
+        fn=graptioner,
+        inputs=[
+            gr.Image(type="pil", label="Upload Image"),
+            gr.Textbox(lines=1, placeholder="Image URL", label="Image URL"),
+        ],
+        outputs=["text"],
+    )
+    iface.launch(share=args.share)