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import os
import streamlit as st
from PIL import Image
import numpy as np
import tempfile
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import classification_report
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.utils import img_to_array
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# Set up the configuration for Streamlit secrets
#api_key = st.secrets["G_Key"]
#genai.configure(api_key=api_key)
# Function to preprocess and load image dataset
def load_images(image_paths):
data, labels = [], []
for label, imagePath in image_paths:
image = Image.open(imagePath)
image = img_to_array(image)
data.append(image)
labels.append(label)
return np.array(data), np.array(labels)
# Function to build and train model
def train_model(image_paths):
data, labels = load_images(image_paths)
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
# Split data into training and testing sets
trainX, testX, trainY, testY = train_test_split(data, labels, test_size=0.25, random_state=42)
# Normalize the data
trainX, testX = trainX / 255.0, testX / 255.0
# Define the model
model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
MaxPooling2D((2, 2)),
Flatten(),
Dense(64, activation='relu'),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(trainX, trainY, validation_data=(testX, testY), epochs=10, batch_size=32)
# Evaluate the model
predictions = model.predict(testX, batch_size=32)
print(classification_report(testY, predictions, target_names=lb.classes_))
return model
# Function to classify image using the trained model
def classify_image(model, image_file):
image = Image.open(image_file)
image = image.resize((64, 64))
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
prediction = model.predict(image)[0]
return "Class 1" if prediction > 0.5 else "Class 2"
st.title("AI Image Classifier")
# User inputs
item1 = st.text_input("Enter the first item to classify (e.g., motorcycle):")
item2 = st.text_input("Enter the second item to classify (e.g., car):")
# Temporary directories for image storage
if item1 and item2:
with tempfile.TemporaryDirectory() as tempdir:
st.write(f"Temporary directory created at {tempdir}")
# Upload images for the first item
st.write(f"Upload images for {item1}")
item1_files = st.file_uploader(f"Choose images for {item1}...", accept_multiple_files=True, type=["jpg", "jpeg", "png"])
# Upload images for the second item
st.write(f"Upload images for {item2}")
item2_files = st.file_uploader(f"Choose images for {item2}...", accept_multiple_files=True, type=["jpg", "jpeg", "png"])
if item1_files and item2_files:
image_paths = []
# Save item1 images to temporary directory
for file in item1_files:
file_path = os.path.join(tempdir, f"{item1}_{file.name}")
with open(file_path, "wb") as f:
f.write(file.getbuffer())
image_paths.append((item1, file_path))
# Save item2 images to temporary directory
for file in item2_files:
file_path = os.path.join(tempdir, f"{item2}_{file.name}")
with open(file_path, "wb") as f:
f.write(file.getbuffer())
image_paths.append((item2, file_path))
# Train the model
model = train_model(image_paths)
st.write(f"Model trained to classify {item1} vs {item2}")
# Upload an image for classification
uploaded_file = st.file_uploader("Choose an image for classification...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None and item1 and item2:
st.image(uploaded_file, caption='Uploaded Image.', use_column_width=True)
st.write("")
st.write("Classifying...")
label = classify_image(model, uploaded_file)
st.write(f'This image is classified as: {label}')
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