Spaces:
Sleeping
Sleeping
File size: 2,086 Bytes
9249488 652d7e3 9249488 3e6b952 9249488 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
import streamlit as st
from keras.models import load_model
from keras.preprocessing.image import load_img, img_to_array
import numpy as np
import os
# Load model
model = load_model('FishModel_VGG16.h5', compile=False)
# Class labels
class_names = ['Bangus', 'Big Head Carp', 'Black Spotted Barb', 'Catfish', 'Climbing Perch',
'Fourfinger Threadfin','Freshwater Eel', 'Glass Perchlet', 'Goby', 'Gold Fish',
'Gourami', 'Grass Carp', 'Green Spotted Puffer', 'Indian Carp', 'Indo-Pacific Tarpon',
'Jaguar Gapote', 'Janitor Fish', 'Knifefish', 'Long-Snouted Pipefish','Mosquito Fish',
'Mudfish', 'Mullet', 'Pangasius', 'Perch', 'Scat Fish', 'Silver Barb', 'Silver Carp',
'Silver Perch', 'Snakehead', 'Tenpounder', 'Tilapia']
# App Title
st.title("🐟 Fish Classifier App")
st.subheader("Identify fish species using a VGG16-based deep learning model.")
# Instructions
st.markdown("""
### 📌 How to Use
1. **Upload** a clear image of a fish (supported formats: JPG, JPEG, PNG).
2. The app will automatically **analyze the image** using a trained deep learning model.
3. You will get the **predicted fish species** along with the **confidence level**.
💡 *Tip: Use centered and well-lit fish images for better results.*
""")
# File uploader
uploaded_file = st.file_uploader("Upload an image of a fish", type=["jpg", "jpeg", "png"])
# Prediction logic
def predict_image(img_path):
img = load_img(img_path, target_size=(224, 224))
img_array = img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
preds = model.predict(img_array)
pred_class = class_names[np.argmax(preds)]
confidence = np.max(preds)
return pred_class, confidence
# Handle uploaded file
if uploaded_file is not None:
st.image(uploaded_file, caption="Uploaded Image", use_container_width=True)
with open("temp.jpg", "wb") as f:
f.write(uploaded_file.read())
label, conf = predict_image("temp.jpg")
st.success(f"Prediction: **{label}** ({conf * 100:.2f}% confidence)")
|