| # import streamlit as st | |
| # import tensorflow as tf | |
| # import numpy as np | |
| # from flask import Flask, jsonify, request | |
| # import requests | |
| # # Create a Flask app | |
| # app = Flask(__name__) | |
| # # Tensorflow Model Prediction | |
| # def model_prediction(test_image): | |
| # model = tf.keras.models.load_model("image_to_food_model1.h5") | |
| # image = tf.keras.preprocessing.image.load_img(test_image, target_size=(256, 256)) | |
| # 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 | |
| # # Define an endpoint to handle predictions | |
| # @app.route('/', methods=['POST']) | |
| # def predict(): | |
| # test_image = request.files['file'] | |
| # result_index = model_prediction(test_image) | |
| # # Reading Labels | |
| # with open("labels.txt") as f: | |
| # content = f.readlines() | |
| # label = [i.strip() for i in content] | |
| # prediction = label[result_index] | |
| # return jsonify({"prediction": prediction}) | |
| # # Sidebar | |
| # st.sidebar.title("Dashboard") | |
| # app_mode = st.sidebar.selectbox("Select Page", ["Prediction"]) | |
| # # Main Page | |
| # # if app_mode == "Home": | |
| # # st.header("Food Recipe") | |
| # # image_path = "home_page.jpg" | |
| # # st.image(image_path) | |
| # # Prediction Page | |
| # if app_mode == "Prediction": | |
| # st.header("Model Prediction") | |
| # test_image = st.file_uploader("Choose an Image:") | |
| # if st.button("Show Image"): | |
| # st.image(test_image, width=4, use_column_width=True) | |
| # # Predict button | |
| # if st.button("Predict"): | |
| # st.write("Our Prediction") | |
| # # Send image to backend for prediction | |
| # files = {'file': test_image.getvalue()} | |
| # response = requests.post('http://localhost:8501', files=files) | |
| # prediction = response.json()["prediction"] | |
| # st.success("Model is Predicting it's a {}".format(prediction)) | |
| import streamlit as st | |
| import tensorflow as tf | |
| import numpy as np | |
| #Tensorflow Model Prediction | |
| def model_prediction(test_image): | |
| model = tf.keras.models.load_model("image_to_food_model1.h5") | |
| image = tf.keras.preprocessing.image.load_img(test_image,target_size=(256,256)) | |
| 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",["Prediction"]) | |
| # #Main Page | |
| # if(app_mode=="Home"): | |
| # st.header("Food Recipe") | |
| # image_path = "home_page.jpg" | |
| # st.image(image_path) | |
| #Prediction Page | |
| if(app_mode=="Prediction"): | |
| st.header("Model Prediction") | |
| test_image = st.file_uploader("Choose an Image:") | |
| if(st.button("Show Image")): | |
| st.image(test_image,width=4,use_column_width=True) | |
| #Predict button | |
| if(st.button("Predict")): | |
| st.snow() | |
| st.write("Our Prediction") | |
| result_index = model_prediction(test_image) | |
| #Reading Labels | |
| with open("labels.txt") as f: | |
| content = f.readlines() | |
| label = [] | |
| for i in content: | |
| label.append(i[:-1]) | |
| st.success("Model is Predicting it's a {}".format(label[result_index])) | |