| import streamlit as st | |
| from PIL import Image | |
| from tensorflow.keras.models import load_model | |
| import numpy as np | |
| model=load_model("model.h5") | |
| st.title("Plant Seedlings Detection") | |
| img=st.camera_input("Camera") | |
| def process_image(input_img): | |
| if input_img.mode == 'RGBA': | |
| input_img = input_img.convert('RGB') | |
| input_img=input_img.resize((170,170)) | |
| input_img=np.array(input_img) | |
| input_img=input_img/255.0 | |
| input_img=np.expand_dims(input_img,axis=0) | |
| return input_img | |
| if img is not None: | |
| img=Image.open(img) | |
| image=process_image(img) | |
| prediction=model.predict(image) | |
| predicted_class=np.argmax(prediction) | |
| class_names=["Black-grass", | |
| "Charlock", | |
| "Cleavers", | |
| "Common Chickweed", | |
| "Common wheat", | |
| "Fat Hen", | |
| "Loose Silky-bent", | |
| "Maize", | |
| "Scentless Mayweed", | |
| "Shepherds Purse", | |
| "Small-flowered Cranesbill", | |
| "Sugar beet"] | |
| st.write(class_names[predicted_class]) |