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#import streamlit as st
import gradio as gr
import os
import csv
import time
# Image Classification Using Torch:
import requests
from PIL import Image
from torchvision import transforms
# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def predict(inp):
inp = transforms.ToTensor()(inp).unsqueeze(0)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
return confidences
import gradio as gr
interface1=gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=3),
examples=["lion.jpg", "cheetah.jpg"])
# Image Classification Using VIT - Vision Image Transformers Architecture
import gradio as gr
interface2=gr.Interface.load(
"huggingface/google/vit-base-patch16-224",
examples=["alligator.jpg", "laptop.jpg"])
# ONNX Model Zoo:
gr.Markdown("""
https://github.com/onnx/models
""")
interface1.launch()
interface2.launch()
uploaded_images = {'characters': {}, 'terrain': {}}
def get_image_path(img, name, image_type):
file_path = f"data/uploadedImages/{image_type}/{name}/{img.name}"
os.makedirs(os.path.dirname(file_path), exist_ok=True)
with open(file_path, "wb") as img_file:
img_file.write(img.getbuffer())
return file_path
def update_csv_file(uploaded_file, name, image_type):
csv_file_path = "Resources.csv"
with open(csv_file_path, mode='a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow([name, uploaded_file.name, image_type])
def get_uploaded_files_info():
csv_file_path = "Resources.csv"
with open(csv_file_path, mode='r') as csv_file:
csv_reader = csv.reader(csv_file)
files_info = []
for row in csv_reader:
files_info.append(row)
return files_info
def display_images_from_csv():
files_info = get_uploaded_files_info()
for row in files_info:
if row[2] == 'characters':
img_path = f"data/uploadedImages/{row[2]}/{row[0]}/{row[1]}"
#st.sidebar.image(img_path, width=100, caption=row[0])
else:
img_path = f"data/uploadedImages/{row[2]}/{row[0]}/{row[1]}"
#st.image(img_path, width=100, caption=row[0])
# Rewrite for gradio
#image_type = gr.selectbox('Choose image type:', options=['characters', 'terrain'])
#name = gr.text_input('Enter a name for the image:')
#uploaded_files = gr.file_uploader('Upload image(s)', type=['png', 'jpg'], accept_multiple_files=True)
for uploaded_file in uploaded_files:
if uploaded_file is not None:
# Get actual image file
bytes_data = get_image_path(uploaded_file, name, image_type)
uploaded_images[image_type].setdefault(name, [])
uploaded_images[image_type][name].append(bytes_data)
# Rewrite for gradio
#gr.image(bytes_data, use_column_width=True)
gr.Markdown(image_type)
gr.Markdown(name)
update_csv_file(uploaded_file, name, image_type)
if image_type == 'characters':
if uploaded_images['characters']:
# Rewrite for gradio
# gr.sidebar.write('**Characters**')
for name, files in uploaded_images['characters'].items():
for file in files:
gr.Markdown("""
""")
gr.Markdown(name)
gr.Markdown(file)
# Rewrite for gradio
#gr.sidebar.image(file, width=100, caption=name)
else:
if uploaded_images['terrain']:
#st.write('**Terrain**')
gr.Markdown("**Terrain**")
row = []
for name, files in uploaded_images['terrain'].items():
for file in files:
row.append(file)
if len(row) == 3:
# Rewrite for gradio
#gr.image(row, width=100 * 3)
row = []
if row:
# Rewrite for gradio
#gr.image(row, width=100 * len(row)) # Last row, if not complete
gr.Markdown(row)
while True:
time.sleep(20)
# Rewrite for gradio
#gr.empty()
display_images_from_csv()
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