feat: ✨ initial commit added

Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>

app.py

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+import os
+import PIL.Image
+import transformers
+from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor
+import torch
+import supervision as sv
+import cv2
+import numpy as np
+from PIL import Image
+import gradio as gr
+
+BOX_ANNOTATOR = sv.BoxAnnotator()
+LABEL_ANNOTATOR = sv.LabelAnnotator()
+MASK_ANNOTATOR = sv.MaskAnnotator()
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+model_id = "google/paligemma2-3b-pt-448"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).eval().to(DEVICE)
+processor = PaliGemmaProcessor.from_pretrained(model_id)
+
+
+
+def process_image(input_image,input_text,class_names):
+    class_list = class_names.split(',')
+    cv_image = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)
+    model_inputs = processor(text=input_text, images=input_image, return_tensors="pt").to(torch.bfloat16).to(model.device)
+    input_len = model_inputs["input_ids"].shape[-1]
+
+    with torch.inference_mode():
+        generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False)
+        generation = generation[0][input_len:]
+        result = processor.decode(generation, skip_special_tokens=True)
+
+    detections = sv.Detections.from_lmm(
+        sv.LMM.PALIGEMMA,
+        result,
+        resolution_wh=(input_image.width, input_image.height),
+        classes=class_list
+    )
+
+    annotated_image = BOX_ANNOTATOR.annotate(
+        scene=cv_image.copy(),
+        detections=detections
+    )
+    annotated_image = LABEL_ANNOTATOR.annotate(
+        scene=annotated_image,
+        detections=detections
+    )
+    annotated_image = MASK_ANNOTATOR.annotate(
+        scene=annotated_image,
+        detections=detections
+    )
+
+    annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+    annotated_image = Image.fromarray(annotated_image)
+
+    return annotated_image, result
+
+
+app = gr.Interface(
+    fn=process_image,
+    inputs=[gr.Image(type="pil"),gr.Textbox(lines=2, placeholder="Enter text here..."), 
+            gr.Textbox(lines=1, placeholder="Enter class names separated by commas...")],
+    outputs=[gr.Image(type="pil"), gr.Textbox()],
+    title="PaliGemma2 Image Detection with Supervision",
+    description="Detect objects in an image using PaliGemma2 model."
+)
+
+
+
+if __name__ == "__main__":
+    app.launch()

requirements.txt

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+supervision
+transformers==4.47.0
+requests
+tqdm
+gradio