app06.py

<<<<<<< HEAD

import streamlit as st
from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
from PIL import Image
import numpy as np
import torch

# Load the model and processor
model_dir = "/home/user/app/defectdetection/model"
model = SegformerForSemanticSegmentation.from_pretrained(model_path)
preprocessor = SegformerImageProcessor.from_pretrained(model_path)

model = SegformerForSemanticSegmentation.from_pretrained(model_dir)
processor = SegformerImageProcessor.from_pretrained(model_dir)
model.eval()

st.title("PCB Defect Detection")

# Upload image in Streamlit
uploaded_file = st.file_uploader("Upload a PCB image", type=["jpg", "png"])

if uploaded_file:
    # Preprocess the image
    test_image = Image.open(uploaded_file).convert("RGB")
    inputs = processor(images=test_image, return_tensors="pt")

    # Model inference
    with torch.no_grad():
        outputs = model(**inputs)

    # Post-process
    semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[test_image.size[::-1]])[0]
    semantic_map = np.uint8(semantic_map)
    semantic_map[semantic_map==1] = 255
    semantic_map[semantic_map==2] = 195
    semantic_map[semantic_map==3] = 135
    semantic_map[semantic_map==4] = 75

    # Display the results
    st.image(test_image, caption="Uploaded Image", use_column_width=True)
    st.image(semantic_map, caption="Predicted Defects", use_column_width=True, channels="GRAY")
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import streamlit as st
from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
from PIL import Image
import numpy as np
import torch

# Load the model and processor
model_dir = "/home/user/app/defectdetection/model"
model = SegformerForSemanticSegmentation.from_pretrained(model_dir)
processor = SegformerImageProcessor.from_pretrained(model_dir)
model.eval()

st.title("PCB Defect Detection")

# Upload image in Streamlit
uploaded_file = st.file_uploader("Upload a PCB image", type=["jpg", "png"])

if uploaded_file:
    # Preprocess the image
    test_image = Image.open(uploaded_file).convert("RGB")
    inputs = processor(images=test_image, return_tensors="pt")

    # Model inference
    with torch.no_grad():
        outputs = model(**inputs)

    # Post-process
    semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[test_image.size[::-1]])[0]
    semantic_map = np.uint8(semantic_map)
    semantic_map[semantic_map==1] = 255
    semantic_map[semantic_map==2] = 195
    semantic_map[semantic_map==3] = 135
    semantic_map[semantic_map==4] = 75

    # Display the results
    st.image(test_image, caption="Uploaded Image", use_column_width=True)
    st.image(semantic_map, caption="Predicted Defects", use_column_width=True, channels="GRAY")
>>>>>>> 36ac725dd03eaeedd3c4601d12a8b80b846b7647