Update app.py

app.py

@@ -11,22 +11,16 @@ st.set_page_config(layout="wide")
 
 login(token=os.getenv("TOKEN"))
 
-try:
-    with open('l.pkl', 'rb') as file:
-        similarity = pickle.load(file)
-except FileNotFoundError:
-    st.error("The similarity file was not found.")
-
-try:
-    with open('items_dict.pkl', 'rb') as file:
-        items_dict = pickle.load(file)
-except FileNotFoundError:
-    st.error("The items dictionary file was not found.")
-
-try:
-    data = pd.read_csv('marketing_sample_for_walmart_com-product_details__20200101_20200331__30k_data.csv')
-except FileNotFoundError:
-    st.error("The CSV file was not found.")
+with open('cv.pkl', 'rb') as file:
+    cv = pickle.load(file)
+
+with open('vectors.pkl', 'rb') as file:
+    vectors = pickle.load(file)
+
+with open('items_dict.pkl', 'rb') as file:
+    items_dict = pd.DataFrame.from_dict(pickle.load(file))
+
+data = pd.read_csv('marketing_sample_for_walmart_com-product_details__20200101_20200331__30k_data.csv')
 
 # Initialize the inference client for the Mixtral model
 client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
@@ -49,6 +43,12 @@ if 'selected_difficulty' not in st.session_state:
 if 'exclusions' not in st.session_state:
     st.session_state.exclusions = None
 
+def get_recommendations(user_description, count_vectorizer, count_matrix):
+    user_description = preprocess_text(user_description)
+    user_vector = count_vectorizer.transform([user_description])
+    cosine_similarities = cosine_similarity(user_vector, count_matrix).flatten()
+    similar_indices = cosine_similarities.argsort()[::-1]
+    return similar_indices
 
 def create_detailed_prompt(user_direction, exclusions, serving_size, difficulty):
     if difficulty == "Quick & Easy":
@@ -239,8 +239,8 @@ if st.session_state.recipe is not None:
         name_and_dis = f'# {recipe["name"]}\n\n'
         name_and_dis += f'{recipe["description"]}\n\n'
         ingredients = '## Ingredients:\n'
-        for ingredient in recipe["ingredients"]:
-            ingredients += f"- {ingredient['name']}\n"
+        # for ingredient in recipe["ingredients"]:
+        #     ingredients += f"- {ingredient['name']}\n"
         instructions = '\n## Instructions:\n'
         for instruction in recipe["instructions"]:
             instructions += f"{instruction['step_number']}. {instruction['instruction']}\n"
@@ -248,10 +248,11 @@ if st.session_state.recipe is not None:
         st.write(name_and_dis)
         col01, col02 = st.columns(2)
         with col01:
-            cont = st.container()
-            cont.write(ingredients)
+            cont = st.container(border=True, height=500)
+            for i in recipe["ingredients"]:
+                cont.selectbox(i['name'],options=items_dict.iloc[get_recommendations(i['name'],cv,vectors)]["Product Name"].values)
         with col02:
-            cont = st.container()
+            cont = st.container(border=True, height=500)
             cont.write(instructions)
     except (json.JSONDecodeError, KeyError) as e:
         st.error(f"Failed to parse recipe: {e}")