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Update u.py

4185b9c verified over 2 years ago

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import subprocess
import sys

def install(package):
    subprocess.check_call([sys.executable, "-m", "pip", "install", package])

install('fastai')
install('torch')

import streamlit as st
from fastai.vision.all import *
from PIL import Image

# Load the model (ensure model file is in the same directory or provide correct path)
@st.cache(allow_output_mutation=True)
def load_model():
    # Replace 'model.pkl' with your model file
    model = load_learner('modelmarvel.pkl')
    return model

# Load the model
model = load_model()

# Streamlit application
st.title("Image Classification with fastai")

# Image upload
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

if uploaded_file:
    # Open and display the image
    image = Image.open(uploaded_file).convert("RGB")
    st.image(image, caption="Uploaded Image", use_column_width=True)
    
    # Prepare image for the model
    img = PILImage.create(uploaded_file)
    
    # Predict
    pred, pred_idx, probs = model.predict(img)
    
    # Display results
    st.write(f"Prediction: {pred}")
    st.write(f"Probability: {probs[pred_idx]:.4f}")

    # Optionally, display confidence as a bar chart
    st.bar_chart(probs.numpy())

	import subprocess
	import sys

	def install(package):
	subprocess.check_call([sys.executable, "-m", "pip", "install", package])

	install('fastai')
	install('torch')

	import streamlit as st
	from fastai.vision.all import *
	from PIL import Image

	# Load the model (ensure model file is in the same directory or provide correct path)
	@st.cache(allow_output_mutation=True)
	def load_model():
	# Replace 'model.pkl' with your model file
	model = load_learner('modelmarvel.pkl')
	return model

	# Load the model
	model = load_model()

	# Streamlit application
	st.title("Image Classification with fastai")

	# Image upload
	uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

	if uploaded_file:
	# Open and display the image
	image = Image.open(uploaded_file).convert("RGB")
	st.image(image, caption="Uploaded Image", use_column_width=True)

	# Prepare image for the model
	img = PILImage.create(uploaded_file)

	# Predict
	pred, pred_idx, probs = model.predict(img)

	# Display results
	st.write(f"Prediction: {pred}")
	st.write(f"Probability: {probs[pred_idx]:.4f}")

	# Optionally, display confidence as a bar chart
	st.bar_chart(probs.numpy())