app.py

!pip install gradio
!pip install openai
!pip install PyPDF2
!pip install tiktoken
!pip install python-pptx
!apt-get install git
import gradio as gr

!git clone https://github.com/facebookresearch/segment-anything-2
%cd /content/segment-anything-2

!git checkout sam2.1
# Install the package
!pip install .

!wget -O sam2_hiera_tiny.pt "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt"
!wget -O sam2_hiera_small.pt "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_small.pt"
!wget -O sam2_hiera_base_plus.pt "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_base_plus.pt"
!wget -O sam2_hiera_large.pt "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_large.pt"

import os
#import gradio as gr
import numpy as np
import pandas as pd
import cv2
import torch
import torch.nn as nn
from PIL import Image
import matplotlib.pyplot as plt
import seaborn as sns
from fastai.vision import *
from fastai.vision.all import *
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import tensorflow as tf
import re
import json
import ast
import openai
import tiktoken
import shutil
import concurrent
import textwrap
from time import sleep
from csv import writer
from tqdm import tqdm
from scipy import spatial
from pptx import Presentation  # for PowerPoint
from PyPDF2 import PdfReader
from openai import OpenAI
from IPython.display import display, Markdown, Latex, HTML
from transformers import GPT2Tokenizer
#from transformers import AutoTokenizer
from google.colab import files  # for uploading files
from termcolor import colored  # for colored text output

%matplotlib inline
%config InlineBackend.figure_format='retina'

%cd /content/segment-anything-2

from sam2.build_sam import build_sam2
from sam2.sam2_image_predictor import SAM2ImagePredictor

sam2_checkpoint = "sam2_hiera_small.pt"
model_cfg = "sam2_hiera_s.yaml"

sam2_model = build_sam2(model_cfg, sam2_checkpoint, device="cuda")
predictor = SAM2ImagePredictor(sam2_model)

checkpoint_path = "/root/.cache/kagglehub/models/ybhavsar/newsegmentation/PyTorch/default/1/sam2_lr0.0001_wd0.01_900.torch"
predictor.model.load_state_dict(torch.load(checkpoint_path))