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import alt as alt
import streamlit as st
import pandas as pd
import tensorflow as tf
import altair as alt
from utils import load_and_prep, get_classes, preprocess_data # Import the preprocess_data function
import time
# @st.cache_data(suppress_st_warning=True)
def predicting(image, model):
image = load_and_prep(image)
image = tf.cast(tf.expand_dims(image, axis=0), tf.int16)
preds = model.predict(image)
pred_class = class_names[tf.argmax(preds[0])]
pred_conf = tf.reduce_max(preds[0])
top_5_i = sorted((preds.argsort())[0][-5:][::-1])
values = preds[0][top_5_i] * 100
labels = []
for x in range(5):
labels.append(class_names[top_5_i[x]])
df = pd.DataFrame({"Top 5 Predictions": labels,
"F1 Scores": values,
'color': ['#EC5953', '#EC5953', '#EC5953', '#EC5953', '#EC5953']})
df = df.sort_values('F1 Scores')
return pred_class, pred_conf, df
class_names = get_classes()
st.set_page_config(page_title="Dish Decoder",
page_icon="🍔")
#### SideBar ####
st.sidebar.title("What's Dish Decoder ?")
st.sidebar.write("""
Dish Decoder is an end-to-end **CNN Image Classification Model** which identifies the food in your image.
- It can identify over 100 different food classes
- It is based upon a pre-trained Image Classification Model that comes with Keras and then retrained on the infamous **Food101 Dataset**.
- The Model actually beats the DeepFood Paper's model which also trained on the same dataset.
- The Accuracy acquired by DeepFood was 77.4% and our model's 85%.
- Difference of 8% ain't much, but the interesting thing is, DeepFood's model took 2-3 days to train while our's barely took 90min.
**Accuracy :** **`85%`**
**Model :** **`EfficientNetB1`**
**Dataset :** **`Food101`**
""")
#### Main Body ####
st.title("Dish Decoder 🍔👁️")
st.header("Discover, Decode, Delight !")
file = st.file_uploader(label="Upload an image of food.",
type=["jpg", "jpeg", "png"])
model = tf.keras.models.load_model("FoodVision.hdf5")
st.sidebar.markdown("Created by **Sparsh Goyal**")
st.markdown(
"""
<div style="position: fixed; bottom: 0; right: 10px; padding: 10px; color: white;">
<a href="https://github.com/sg-sparsh-goyal" target="_blank" style="color: white; text-decoration: none;">
✨ Github
</a><br>
</div>
""",
unsafe_allow_html=True
)
if not file:
st.warning("Please upload an image")
st.stop()
else:
st.info("Uploading your image...")
# Add a loading bar
progress_bar = st.progress(0)
image = file.read()
# Simulate image processing with a 2-second delay
for percent_complete in range(100):
time.sleep(0.02)
progress_bar.progress(percent_complete + 1)
st.success("Image upload complete!")
st.image(image, use_column_width=True)
pred_button = st.button("Predict")
if pred_button:
pred_class, pred_conf, df = predicting(image, model)
st.success(f'Prediction : {pred_class} \nConfidence : {pred_conf * 100:.2f}%')
chart = alt.Chart(df).mark_bar(color='#00FF00').encode(
x=alt.X('F1 Scores', axis=alt.Axis(title=None)),
y=alt.Y('Top 5 Predictions', sort=None, axis=alt.Axis(title=None)),
text='F1 Scores'
).properties(width=600, height=400)
st.altair_chart(chart, use_container_width=True)
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