ai,meal planner,profile upgrade

recommendSystem/recommend.py

@@ -2,11 +2,27 @@
 import pandas as pd
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import linear_kernel
+from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient
+import requests
+import json
+from recipeRecDTO import RecipeRecommendationObject 
+
 
 app = Flask(__name__)
 
+sas_token = "sp=racwdyti&st=2024-05-14T12:06:34Z&se=2024-06-04T20:06:34Z&sv=2022-11-02&sr=b&sig=%2FYZk57JEkcpWlOKhm9rl5y2roVMQbwl%2Fa%2FgyPS5uL5A%3D"
+blob_url = "https://blobrecipeimages.blob.core.windows.net/data-set-kaggle/recipes_with_no_tags_and_cuisine.csv?" + sas_token
+#Blob Service Client oluştur
+blob_service_client = BlobServiceClient(account_url="https://blobrecipeimages.blob.core.windows.net", credential=sas_token)
+
+# Container Client oluştur
+container_client = blob_service_client.get_container_client("data-set-kaggle")
+
+#blob_url="https://blobrecipeimages.blob.core.windows.net/data-set-kaggle/recipes_with_no_tags_and_cuisine.csv?"
+
+
 # Load recipe data
-recipe_data = pd.read_csv('https://blobrecipeimages.blob.core.windows.net/data-set-kaggle/recipes_with_no_tags_and_cuisine.csv')
+recipe_data = pd.read_csv(blob_url)
 recipe_data = recipe_data.dropna(subset=['ingredients', 'cuisine_path'])
 recipe_data['total_time'] = recipe_data['total_time'].astype(str)
 recipe_data['combined_features'] = recipe_data['total_time'] + ' ' + recipe_data['ingredients'] + ' ' + recipe_data['cuisine_path']
@@ -15,25 +31,149 @@
 tfidf_vectorizer = TfidfVectorizer(stop_words='english')
 tfidf_matrix = tfidf_vectorizer.fit_transform(recipe_data['combined_features'])
 cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)
+def get_recommendations(title=None, ingredients=None):
+    matching_recipes = recipe_data
+
+    if title:
+        matching_recipes = matching_recipes[matching_recipes['recipe_name'].str.contains(title, case=False)]
+
+    if ingredients:
+        ingredient_query = '|'.join(ingredients)
+        matching_recipes = matching_recipes[matching_recipes['ingredients'].str.contains(ingredient_query, case=False)]
 
-def get_recommendations(query):
-    matching_recipes = recipe_data[recipe_data['recipe_name'].str.contains(query, case=False)]
     if matching_recipes.empty:
         return []
-    selected_recipe = matching_recipes.iloc[0]['recipe_name']
-    idx = recipe_data[recipe_data['recipe_name'] == selected_recipe].index[0]
-    sim_scores = list(enumerate(cosine_sim[idx]))
+
+    combined_features_with_ingredients = matching_recipes['total_time'] + ' ' + matching_recipes['ingredients'] + ' ' + matching_recipes['cuisine_path']
+    tfidf_matrix_with_ingredients = tfidf_vectorizer.fit_transform(combined_features_with_ingredients)
+    cosine_sim_with_ingredients = linear_kernel(tfidf_matrix_with_ingredients, tfidf_matrix_with_ingredients)
+
+    # Calculate similarity scores for matched recipes
+    sim_scores = []
+    for i in range(len(matching_recipes)):
+        sim_scores.append((i, cosine_sim_with_ingredients[i,-1]))  # similarity score for the last recipe
+
     sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
-    sim_recipes = sim_scores[1:11]
-    return recipe_data['recipe_name'].iloc[[i[0] for i in sim_recipes]].tolist()
+    sim_recipes = sim_scores[:5]  # Take the top 5 similar recipes
+    recipe_indices = [i[0] for i in sim_recipes]
+    recommended_recipes = [matching_recipes.iloc[idx] for idx in recipe_indices]
+
+    return recommended_recipes
 
-@app.route('/recommend', methods=['GET'])
+
+
+
+@app.route('/hello', methods=['GET'])
+def hello():
+    return "hellooooo"
+
+
+@app.route('/get-recommendations', methods=['GET'])
 def recommend():
-    query = request.args.get('query')
-    if not query:
-        return jsonify({"error": "No query parameter provided"}), 400
-    recommendations = get_recommendations(query)
-    return jsonify({"recommendations": recommendations})
+    title = request.args.get('title')
+    ingredients = request.args.get('ingredients')
+
+    if not title and not ingredients:
+        return jsonify({'error': 'Please provide at least one parameter.'}), 400
+
+    if ingredients and ":" in ingredients:
+        ingredients = format_data_ingredients(ingredients)
+
+    recommendations = get_recommendations(title, ingredients)
+
+    if not recommendations:
+        return jsonify({'message': 'No recipes found matching the criteria.'}), 404
+
+    # Convert recommendations to RecipeRecommendationObject
+    recommendation_objects = [create_recommendation_object(recipe) for recipe in recommendations]
+
+    return jsonify({'recommendations': [vars(obj) for obj in recommendation_objects]})
+
+def format_data_ingredients(ingredients):
+    formatted_ingredients = []
+    for ingredient in ingredients.split(','):
+        parts = ingredient.strip().split(':')
+        formatted_ingredient = parts[0].strip() if len(parts) > 1 else parts[0].strip()
+        formatted_ingredients.append(formatted_ingredient)
+    return formatted_ingredients
+
+
+def create_recommendation_object(recipe_row):
+    return RecipeRecommendationObject (
+        title=recipe_row['recipe_name'],
+        ingredients=recipe_row['ingredients'],
+        description=recipe_row['directions'],  
+        cuisine=recipe_row['cuisine_path'],  
+        timing=recipe_row['total_time'],
+        photoPathURL=recipe_row['img_src']
+    )
+
+@app.route('/add-recipe-to-dataset', methods=['POST'])
+def add_recipe_to_dataset():
+
+    global recipe_data  # Declare recipe_data as global
+
+    data = request.json
+
+    # Extract data from request body
+    recipe_name = data.get('title', '')
+    prep_time = format_time(data.get('preparationTime', None))
+    cook_time = None  
+    total_time = None  
+    servings = None  
+    yield_value = None  
+    ingredients = data.get('ingredients', '')
+    directions = data.get('description', '')
+    rating = None  
+    url = None  
+    cuisine_path = data.get('cuisine', '')
+    nutrition = None  
+    timing = None
+    img_src = data.get('photoPathURL', '')
+
+    # Process the data and add it to the dataset
+    # You can perform any necessary processing here, such as data validation and formatting
+    # Then add the recipe to the dataset
+    new_recipe = pd.DataFrame.from_dict({
+        'recipe_name': [recipe_name],
+        'prep_time': [prep_time],
+        'cook_time': [cook_time],
+        'total_time': [total_time],
+        'servings': [servings],
+        'yield': [yield_value],
+        'ingredients': [ingredients],
+        'directions': [directions],
+        'rating': [rating],
+        'url': [url],
+        'cuisine_path': [cuisine_path],
+        'nutrition': [nutrition],
+        'timing': [timing],
+        'img_src': [img_src]
+    })
+
+    recipe_data = pd.concat([recipe_data, new_recipe], ignore_index=True)
+
+    # Dosyayı bloba yükle
+    data = recipe_data.to_csv(index=False)
+    blob_client = container_client.get_blob_client(blob="recipes_with_no_tags_and_cuisine.csv")
+    blob_client.upload_blob(data, overwrite=True)
+
+    print("Veri başarıyla bloba yüklendi.")
+
+    return jsonify({"message": "Recipe added to dataset successfully"})
+
+def format_time(minutes):
+    if not minutes:
+        return ''
+
+    hours, mins = divmod(minutes, 60)
+
+    if hours > 0 and mins > 0:
+        return f"{hours} hrs {mins} mins"
+    elif hours > 0:
+        return f"{hours} hrs"
+    else:
+        return f"{mins} mins"
 
 if __name__ == '__main__':
-    app.run(debug=True)
+    app.run()

scripts/userEditProfile.js

@@ -5,9 +5,7 @@ document.addEventListener('DOMContentLoaded', function () {
     ];
 
     var allergicFoodSection = document.querySelector('.allergic-food-section');
-    var otherFoodContainer = document.getElementById('other-food-container');
 
-    // Checkboxları oluştur
     allergicFoods.forEach(function(food) {
         var checkbox = document.createElement('input');
         checkbox.type = 'checkbox';
@@ -22,13 +20,6 @@ document.addEventListener('DOMContentLoaded', function () {
         allergicFoodSection.appendChild(document.createElement('br'));
     });
 
-    // Diğer seçeneği için text kutusu oluştur
-    var otherInput = document.createElement('input');
-    otherInput.type = 'text';
-    otherInput.placeholder = 'Type: Allergy 1, Allergy 2';
-    otherInput.id = 'other-food-input';
-    otherFoodContainer.appendChild(otherInput);
-
     var photoUpload = document.getElementById('photo-upload');
     var photoFrame = document.getElementById('photo-frame');
 
@@ -47,74 +38,62 @@ document.addEventListener('DOMContentLoaded', function () {
     });
 });
 
-
 const saveButton = document.getElementById("save-button");
 saveButton.addEventListener("click", async function (event) {
-    event.preventDefault(); // Sayfanın yeniden yüklenmesini önle
+    event.preventDefault();
 
     try {
-        // Kullanıcının girdiği bilgileri al
         const bio = document.getElementById('bio').value;
 
-        // Seçilen alerjenleri al
         const selectedFoods = [];
         document.querySelectorAll('input[name="allergic-food"]:checked').forEach(function(checkbox) {
             selectedFoods.push(checkbox.value);
         });
 
-        // Diğer alerjeni al
-        let otherFoodInput = document.getElementById('other-food-input');
+        const otherFoodInput = document.getElementById('other-food-input');
         if (otherFoodInput) {
             const otherFoodValue = otherFoodInput.value.trim();
             if (otherFoodValue !== '') {
                 selectedFoods.push(otherFoodValue);
             }
         }
 
-        // Boşlukları virgülle değiştir
         const finalAllergiesString = selectedFoods.map(food => food.trim()).join(',');
 
-        // Kullanıcının girdiği diğer alanları al
         const newPassword = document.getElementById('new-password').value;
         const confirmPassword = document.getElementById('confirm-password').value;
 
-        // Kullanıcı fotoğrafını al
         const photoUpload = document.getElementById('photo-upload');
         const photoFile = photoUpload.files[0];
 
-        // Profil verilerini oluştur
-        const profileData = {
-            password: newPassword,
-            bio: bio,
-            allergicFoods: finalAllergiesString,
-            profilePhoto: photoFile
-        };
+        const formData = new FormData();
+        formData.append('password', newPassword);
+        formData.append('bio', bio);
+        formData.append('allergicFoods', finalAllergiesString);
+        if (photoFile) {
+            formData.append('profilePhoto', photoFile);
+        }
 
-        console.log(selectedFoods);
+        console.log('Selected Allergies:', selectedFoods);
         console.log('Final Allergic Foods String:', finalAllergiesString);
 
-        // Profil verilerini kaydetme fonksiyonunu çağır
-        await saveProfile(profileData);
+        await saveProfile(formData);
     } catch (error) {
         console.error('Error saving profile:', error);
-        // Hata durumunda kullanıcıya bildirim gönderilebilir
-        // Örn: alert('Error saving profile: ' + error.message);
     }
 });
 
-
-async function saveProfile(profileData) {
+async function saveProfile(formData) {
     const apiUrl = 'https://recipiebeckend.azurewebsites.net/user/save-user-profile';
     const JWTAccessToken = sessionStorage.getItem('accessToken');
 
     try {
         const response = await fetch(apiUrl, {
             method: 'PUT',
             headers: {
-                'Authorization': JWTAccessToken,
-                'Content-Type': 'application/json'
+                'Authorization': JWTAccessToken
             },
-            body: JSON.stringify(profileData),
+            body: formData
         });
 
         if (!response.ok) {