Create app.py

app.py

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+from transformers import pipeline
+from PIL import Image
+import gradio as gr
+import io
+
+# Load pipeline on CPU
+# device=-1 forces CPU usage
+pipe = pipeline("image-to-text", model="dragonstar/image-text-captcha-v2", device=-1)
+
+def run_ocr(image):
+    """
+    image: PIL Image (Gradio will give a PIL if type="pil")
+    Returns: detected text (string) or error message
+    """
+    if image is None:
+        return "No image provided"
+    try:
+        # pipeline usually expects PIL/np image
+        outputs = pipe(image)
+        # model outputs vary; try common keys
+        if isinstance(outputs, list) and len(outputs) > 0:
+            # Many vision->text models return [{'generated_text': '...'}]
+            first = outputs[0]
+            text = first.get("generated_text") or first.get("text") or str(first)
+        else:
+            text = str(outputs)
+        text = text.strip()
+        if not text:
+            return "No text extracted"
+        return text
+    except Exception as e:
+        return f"Error: {e}"
+
+title = "Image → Text (captcha) — dragonstar/image-text-captcha-v2"
+desc = "Upload a captcha image. Model runs on CPU in this Space. Results may be slower than cloud inference."
+
+demo = gr.Interface(
+    fn=run_ocr,
+    inputs=gr.Image(type="pil", label="Captcha image"),
+    outputs=gr.Textbox(label="Detected text"),
+    title=title,
+    description=desc,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()