Update app.py

app.py

@@ -1,4 +1,3 @@
-import os
 import spaces
 import gradio as gr
 from transformers import AutoProcessor, AutoModelForCausalLM
@@ -15,16 +14,6 @@ import matplotlib.patches as patches
 import random
 import numpy as np
 
-from transformers import AutoProcessor, AutoModelForCausalLM
-from detectron2.config import get_cfg
-from detectron2.engine import DefaultPredictor
-from detectron2 import model_zoo
-from detectron2.utils.visualizer import Visualizer, ColorMode
-from detectron2.data import MetadataCatalog
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-
 import subprocess
 subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
 
@@ -140,22 +129,6 @@ single_task_list =[
     'Object Detection'
 ]
 
-# Detectron2 configuration
-cfg = get_cfg()
-cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
-cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
-cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
-cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-predictor = DefaultPredictor(cfg)
-
-def process_image_with_detectron2(image_name):
-    image_path = os.path.join(example_image_dir, image_name)
-    image = cv2.imread(image_path)
-    outputs = predictor(image)
-    v = Visualizer(image[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
-    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-    return Image.fromarray(out.get_image()[:, :, ::-1])
-
 with gr.Blocks(theme="sudeepshouche/minimalist") as demo:
     gr.Markdown("## 🧬OmniScience - building teams of fine tuned VLM models for diagnosis and detection 🔧")
     gr.Markdown("- 🔬Florence-2 Model Proof of Concept, focusing on Object Detection <OD> tasks.")
@@ -198,16 +171,6 @@ with gr.Blocks(theme="sudeepshouche/minimalist") as demo:
 
         submit_btn.click(process_image, [input_img, task_prompt, model_selector], [output_text, output_img])
 
-    with gr.Tab(label="Segmentation"):
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Dropdown(choices=example_images, label="Input Picture", value=example_images[0])
-                submit_btn = gr.Button(value="Submit")
-            with gr.Column():
-                output_img = gr.Image(label="Output Image")
-
-        submit_btn.click(process_segmentation, inputs=[input_img], outputs=[output_img])
-    
     gr.Markdown("## 🚀Other Cool Stuff:")
     gr.Markdown("- [Florence 2 Whitepaper](https://arxiv.org/pdf/2311.06242) - how I found out about the Roboflow 100 and the BCCD dataset.  Turns out this nugget was from the original [Florence whitepaper](https://arxiv.org/pdf/2111.11432) but useful all the same!")
     gr.Markdown("- [Roboflow YouTube Video on Florence 2 fine-tuning](https://youtu.be/i3KjYgxNH6w?si=x1ZMg9hsNe25Y19-&t=1296) - bookmarked an 🧠insightful trade-off analysis of various VLMs.")