code update

app.py

@@ -1,109 +1,119 @@
-import streamlit as st
-import numpy as np
-import time
-
-import tensorflow as tf
-from utils import load_prepare_image, model_pred, fetch_recipe
-
-import sys
-from RecipeData import fetchRecipeData
-
-IMG_SIZE = (224, 224)
-model_V1 = 'Seefood_model_v1.tflite'
-model_V2 = 'Seefood_model_V2.tflite'
-
-@st.cache()
-def model_prediction(model, img_file, rescale):
-    img = load_prepare_image(img_file, IMG_SIZE, rescale=rescale)
-    prediction = model_pred(model, img)
-    sorceCode, recipe_data = fetchRecipeData(prediction)
-    return prediction, sorceCode, recipe_data 
-
-
-def main():
-    st.set_page_config(
-        page_title="SeeFood",
-        page_icon="🍔",
-        layout="wide",
-        initial_sidebar_state="expanded"
-    )
-
-    st.title('SeeFood🍔')
-    st.write('Upload a food image and get the recipe for that food and other details of that food')
-
-    col1, col2 = st.columns(2)
-
-    with col1: 
-        # image uploading button
-        uploaded_file = st.file_uploader("Choose a file")
-        selected_model = st.selectbox('Select Model',('model 1', 'model 2'), index=1)
-        if uploaded_file is not None:
-            uploaded_img = uploaded_file.read()
-
-            col2.image(uploaded_file, width=500)
-
-        # butoon to make predictions
-        predict = st.button('Get Recipe!')
-
-    if predict:
-        if uploaded_file is not None:
-            with st.spinner('Please Wait 👩‍🍳'):
-
-                # setting model and rescalling 
-                if selected_model == 'model 2':
-                    pred_model = model_V2 
-                    pred_rescale = True
-                else:
-                    pred_model = model_V1 
-                    pred_rescale = False
-                
-                # makeing prediction and fetching food recipe form api
-                food, source_code, recipe_data = model_prediction(pred_model, uploaded_img, pred_rescale)
-                
-                # asssigning caleoric breakdown data
-                percent_Protein = recipe_data['percentProtein']
-                percent_fat = recipe_data['percentFat']
-                percent_carbs = recipe_data['percentCarbs'] 
-                
-                # food name message
-                col1.success(f"It's an {food}")
-                
-                if source_code == 200:
-                    # desplay food recipe
-                    st.header(recipe_data['title']+" Recipe")
-                
-                    col3, col4 = st.columns(2)
-
-                    with col3:
-                        # Ingridents of recipie
-                        st.subheader('Ingredients')
-                        # st.info(recipe_data['ingridents'])
-                        for i in recipe_data['ingridents']:
-                            st.info(f"{i}")
-                    # Inctuction for recipe
-                    with col4:
-                        st.subheader('Instructions')
-                        st.info(recipe_data['instructions'])
-                        # st.subheader('Caloric Breakdown')
-                        '''
-                        ## Caloric Breakdown
-                        '''
-                        st.success(f'''
-                                    * Protien:  {percent_Protein}%
-                                    * Fat: {percent_fat}%
-                                    * Carbohydrates: {percent_carbs}%
-                                    ''')
-                        
-                       
-                else:
-                    st.error('Something went wrong please try again :(')
-                
-
-        else:
-            st.warning('Please Upload Image')
-
-                
-
-
-if __name__=='__main__': 
+import streamlit as st
+import numpy as np
+import time
+
+import tensorflow as tf
+from utils import load_prepare_image, model_pred, fetch_recipe
+from FoodNoFood import food_not_food
+
+import sys
+from RecipeData import fetchRecipeData
+
+IMG_SIZE = (224, 224)
+model_V1 = 'Seefood_model_v1.tflite'
+model_V2 = 'Seefood_model_V2.tflite'
+
+@st.cache()
+def model_prediction(model, img_file, rescale):
+    img = load_prepare_image(img_file, IMG_SIZE, rescale=rescale)
+    prediction = model_pred(model, img)
+    sorceCode, recipe_data = fetchRecipeData(prediction)
+    return prediction, sorceCode, recipe_data 
+
+
+def main():
+    st.set_page_config(
+        page_title="SeeFood",
+        page_icon="🍔",
+        layout="wide",
+        initial_sidebar_state="expanded"
+    )
+
+    st.title('SeeFood🍔')
+    st.write('Upload a food image and get the recipe for that food and other details of that food')
+
+    col1, col2 = st.columns(2)
+
+    with col1: 
+        # image uploading button
+        uploaded_file = st.file_uploader("Choose a file")
+        selected_model = st.selectbox('Select Model',('model 1', 'model 2'), index=1)
+        if uploaded_file is not None:
+            uploaded_img = uploaded_file.read()
+            pil_img = Image.open(uploaded_file)
+
+            col2.image(uploaded_file, width=500)
+
+        # butoon to make predictions
+        predict = st.button('Get Recipe!')
+
+    if predict:
+        with st.spinner("Analyzing Image 🕵️‍♂️"):
+            food_cat = food_not_food(pil_img)
+        
+        if food_cat == 'food':
+            if uploaded_file is not None:
+                with st.spinner('Please Wait 👩‍🍳'):
+
+                    # setting model and rescalling 
+                    if selected_model == 'model 2':
+                        pred_model = model_V2 
+                        pred_rescale = True
+                    else:
+                        pred_model = model_V1 
+                        pred_rescale = False
+                    
+                    # makeing prediction and fetching food recipe form api
+                    food, source_code, recipe_data = model_prediction(pred_model, uploaded_img, pred_rescale)
+                    
+                    # asssigning caleoric breakdown data
+                    percent_Protein = recipe_data['percentProtein']
+                    percent_fat = recipe_data['percentFat']
+                    percent_carbs = recipe_data['percentCarbs'] 
+                    
+                    # food name message
+                    col1.success(f"It's an {food}")
+                    
+                    if source_code == 200:
+                        # desplay food recipe
+                        st.header(recipe_data['title']+" Recipe")
+                    
+                        col3, col4 = st.columns(2)
+
+                        with col3:
+                            # Ingridents of recipie
+                            st.subheader('Ingredients')
+                            # st.info(recipe_data['ingridents'])
+                            for i in recipe_data['ingridents']:
+                                st.info(f"{i}")
+                        # Inctuction for recipe
+                        with col4:
+                            st.subheader('Instructions')
+                            st.info(recipe_data['instructions'])
+                            # st.subheader('Caloric Breakdown')
+                            '''
+                            ## Caloric Breakdown
+                            '''
+                            st.success(f'''
+                                        * Protien:  {percent_Protein}%
+                                        * Fat: {percent_fat}%
+                                        * Carbohydrates: {percent_carbs}%
+                                        ''')
+                            
+                        
+                    else:
+                        st.error('Something went wrong please try again :(')
+                        
+        elif food_cat == 'not food':
+            with col1:
+                st.warning('Invalid Image Please Add Food Image 👨‍🔧')
+                    
+
+        else:
+            st.warning('Please Upload Image')
+
+                
+
+
+if __name__=='__main__': 
     main()