Create app.py

app.py

@@ -0,0 +1,107 @@
+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)