Upload 5 files

labels.txt

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+apple
+banana
+beetroot
+bell pepper
+cabbage
+capsicum
+carrot
+cauliflower
+chilli pepper
+corn
+cucumber
+eggplant
+garlic
+ginger
+grapes
+jalepeno
+kiwi
+lemon
+lettuce
+mango
+onion
+orange
+paprika
+pear
+peas
+pineapple
+pomegranate
+potato
+raddish
+soy beans
+spinach
+sweetcorn
+sweetpotato
+tomato
+turnip
+watermelon

main.py

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+import streamlit as st
+from streamlit_option_menu import option_menu
+import tensorflow as tf
+import numpy as np
+
+# Tensorflow Model Prediction
+def model_prediction(test_image):
+    model = tf.keras.models.load_model("trained_model.h5")
+    image = tf.keras.preprocessing.image.load_img(
+        test_image, target_size=(64, 64))
+    input_arr = tf.keras.preprocessing.image.img_to_array(image)
+    input_arr = np.array([input_arr])  # convert single image to batch
+    predictions = model.predict(input_arr)
+    return np.argmax(predictions)  # return index of max element
+
+# Sidebar
+# st.sidebar.title("Dashboard")
+# app_mode = st.sidebar.selectbox("Select Page",["Home","About Project","Prediction"])
+
+
+with open("style.css") as f:
+    st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+app_mode = option_menu(
+    menu_title=None,
+    options=["Home", "Prediction"],
+    icons=["house-door", "graph-up-arrow"],
+    orientation="horizontal",
+    styles={
+        "container": {
+                "padding": "0!important",
+        },
+        "icon": {
+            "font-size": "20px",
+        },
+        "nav-link": {
+            "font-size": "20px",
+            "margin": "0px",
+            "padding": "7px 0 7px 0",
+        },
+        "nav-link-selected": {
+            "font-weight": "100",
+        }
+    }
+)
+
+# Home Page
+if app_mode == "Home":
+    st.header("Fruits & Vegetables Recognition System")
+    image_path = "fruits-vegetables-banner.jpg"
+    st.image(image_path)
+    st.subheader("About Project")
+    st.markdown("The Fruits and Vegetables Recognition System is an innovative project leveraging Convolutional Neural Networks (CNN) in deep learning to accurately identify and classify various fruits and vegetables. The system utilizes CNN architecture to extract features from input images, enabling accurate classification of fruits and vegetables.")
+    st.markdown(
+        "This Model is able to identify 36 different classes of Fruits and Vegetables.")
+    st.markdown(
+        "Fruits - banana, apple, pear, grapes, orange, kiwi, watermelon, pomegranate, pineapple, mango.")
+    st.markdown("Vegetables - cucumber, carrot, capsicum, onion, potato, lemon, tomato, raddish, beetroot, cabbage, lettuce, spinach, soy bean, cauliflower, bell pepper, chilli pepper, turnip, corn, sweetcorn, sweet potato, paprika, jalepeño, ginger, garlic, peas, eggplant.")
+
+# Prediction Page
+elif app_mode == "Prediction":
+    st.header("Model Prediction")
+    # reading labels
+    with open("labels.txt") as f:
+        content = f.readlines()
+    label = []
+    for i in content:
+        label.append(i[:-1])
+    test_image = st.file_uploader(
+        "Choose an Image:", type=["jpg", "jpeg", "png"])
+    if test_image:
+        st.image(test_image, width=2, use_column_width=True)
+        # predict button
+        if st.button("Predict"):
+            result_index = model_prediction(test_image)
+            st.subheader(f"Model Prediction: {label[result_index]}")
+

style.css

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+div.block-container.st-emotion-cache-1y4p8pa.ea3mdgi5
+{
+    padding-top:30px;
+}
+section.main.st-emotion-cache-uf99v8.ea3mdgi8
+{
+    padding-left:0px;
+    padding-right:0px;
+}
+h2
+{
+    padding-bottom:30px;
+    padding-top:0px;
+    font-size:40px;
+}
+h3
+{
+    font-size:30px;
+}
+p
+{
+    font-size:18px;
+}
+@media(max-width:450px) {
+    h2
+    {
+        font-size:33px;
+    }
+    h3
+    {
+        font-size:27px;
+    }
+}

trained_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:37635af4c895434d4994d9450053f61631993d778feb8bb3011f53659494ad1b
+size 79620032