first commit

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import gspread
+import random
+import requests
+import altair as alt
+from PIL import Image
+
+
+logo_url = "https://www.gradina-slavu.ro/"
+logo_path = "https://static.wixstatic.com/media/268e9b_fb1da1f5fc304d15beee1d2e581d5d0c~mv2.png/v1/fill/w_134,h_62,al_c,q_85,usm_0.66_1.00_0.01,enc_auto/nume.png"
+
+# Display the logo with a black background and centered alignment
+st.markdown(
+    f"""
+    <div style='background-color: rgba(128,128,128,0.4); text-align: center;'>
+        <a href='{logo_url}' target='_blank'>
+            <img src='{logo_path}' width='150'>
+        </a>
+    </div>
+    """,
+    unsafe_allow_html=True,
+)
+
+st.title('RETETE - Selecteaza produs din sidebar')
+st.write(' ')
+
+sa= gspread.service_account(filename='creds.json')
+# Open the Google Sheets file
+sh = sa.open_by_url("https://docs.google.com/spreadsheets/d/1GZjbDLYTgRhKHtvgT868ursHYzXXmHF23w86-3KNZN0/edit#gid=489241774")
+
+worksheet=sh.get_worksheet(0)
+worksheet1=sh.get_worksheet(1)
+worksheet2=sh.get_worksheet(2)
+
+rows = worksheet.get_all_values()
+rows1 = worksheet1.get_all_values()
+rows2 = worksheet2.get_all_values()
+df=pd.DataFrame(rows)
+df1=pd.DataFrame(rows1)
+df2=pd.DataFrame(rows2)
+df2=df2.iloc[1:]
+
+
+options=['stevie','rosii cherry','rosii tocate','suc de rosii','rosii cherry uscate','pesto de busuioc','carne vegetala','pate de ciuperci','zacusca de vinete','zacusca de peste','zacusca de fasole','magiun de prune','hrean in otet','legume uscate']
+valori_optime=['2000','280','25','50','70','20','1600','17000','50','5000','75','5','10','80','1.4','1.6','18','2','400','6','6','550','12','1000','15','350','1000','3500','2400','15','2','5','35','3.5','300','2']
+selected_option = st.sidebar.selectbox("Selecteaza un produs", options)
+
+
+def select_random_value(df, selected_option):
+    # Select a random item from the list
+    selected_item = selected_option 
+    
+    # Find the corresponding values in the dataframe
+    matching_rows = df[df[3] == selected_item]
+    corresponding_values = matching_rows[0].tolist()
+    
+    # Select a random value from the corresponding values
+    if corresponding_values:
+        selected_value = random.choice(corresponding_values)
+        return selected_value
+    else:
+        return None
+
+reteta_selectata=select_random_value(df1,selected_option)
+
+d2 = df2.set_index(0).to_dict('index')
+d2 = {k: {k2: float(v2) for k2, v2 in v.items()} for k, v in d2.items()}
+
+discarded_keys=['Zaharuri','Grasimi saturate','Omega-6','Vit A','Vit C','Vit D','Vit E','Vit K','Pantothenic acid-B5','Choline-B4','Betaine','Sodiu','Cupru','Mangan','Seleniu','Fluor','Cholesterol','Phytosterols']
+
+def select_optimized_keys(d, value, optimals, discarded_keys):
+    optimals = [float(x) for x in optimals]
+    
+    # Normalize the values for all keys except discarded ones
+    normalized = {}
+    for k, v in d.items():
+        if k not in discarded_keys:
+            values = [v2 for k2, v2 in v.items() if k2 not in discarded_keys]
+            min_value, max_value = min(values), max(values)
+            normalized[k] = {k2: (v2 - min_value) / (max_value - min_value) for k2, v2 in v.items() if k2 not in discarded_keys}
+
+    matching_key = [k for k, v in d.items() if k == value][0]
+    other_keys = [k for k in d.keys() if k != matching_key]
+    
+    # Choose a random second key
+    second_key = random.choice(other_keys)
+    
+    # Pick the 6 parameters with bigger gaps for optimizing the third key
+    gaps = {}
+    for k, v in normalized.items():
+        if k != matching_key and k != second_key:
+            gaps[k] = max(v.values()) - min(v.values())
+    sorted_gaps = sorted(gaps.items(), key=lambda x: x[1], reverse=True)
+    third_keys = [x[0] for x in sorted_gaps][:8]
+    
+    def difference(key):
+        return sum(np.abs(sum([normalized[k][j] for k in [second_key, key]]) * 4 - optimals[i]) for i, j in enumerate(normalized[matching_key].keys()))
+
+    # Optimize the third key with the 6 parameters with bigger gaps
+    optimized_key = min(third_keys, key=difference)
+    
+    return [second_key, optimized_key, matching_key]
+selected_keys=select_optimized_keys(d2, reteta_selectata, valori_optime,discarded_keys)
+selected_keys=list(selected_keys)
+selected_keys.reverse()
+
+
+def search_values(lst, dct):
+    result = {}
+    for item in lst:
+        for key, value in dct.items():
+            if item == key:
+                result[item] = value
+    return result
+
+# create a dictionary to hold the new dataframes
+dfs = {}
+
+# loop through the categories and create a new dataframe for each one
+for reteta in selected_keys:
+    dfs[reteta] = df1[df1[0] == reteta]
+
+dfs2={}
+for reteta in selected_keys:
+    dfs2[reteta] = df1[df1[0] == reteta].iloc[:,6:]
+
+
+def make_clickable(url):
+    return f'<a href="{url}" target="_blank">{url}</a>'
+
+for i in range(len(selected_keys)):
+    dfs1 = dfs[selected_keys[i]]
+    dfs1 = pd.DataFrame(dfs1)
+    images_displayed = False
+    # update 'path' column with formatted URLs
+    dfs1[6] = dfs1[6].apply(make_clickable)
+    for index, row in dfs1.iterrows():
+        image_path = row[6]
+        if not image_path:
+            continue
+        # extract URL from formatted string
+        url = image_path.split('"')[1]
+        if not url.startswith('http'):
+            continue
+        try:
+            image = Image.open(requests.get(url, stream=True).raw)
+            image = image.resize((300,300))
+            with st.container():
+              col1, col2, col3 = st.columns([1, 2, 1])
+              col2.image(image, caption=selected_keys[i], width=300, use_column_width=True)
+              images_displayed = True
+        except:
+            continue
+    if images_displayed:
+        dfs1 = dfs1.iloc[:,3:6]
+        dfs1 = dfs1.reset_index(drop=True)
+        indexcol = ['Ingrediente', 'Gramaj', 'Preparare']
+        dfs1.columns = indexcol
+        st.subheader(selected_keys[i])
+        st.write(dfs1,index=False)
+        
+    else:
+        st.warning(f"No image found for {selected_keys[i]}")
+    
+   
+    
+
+final_result=search_values(selected_keys,d2)
+
+Finaldf=pd.DataFrame(final_result)
+Finaldf=Finaldf.round(0)
+new_index=['Calorii','Carbohidrati','Fibre dietetice','Zaharuri','Grasimi','Grasimi saturate','Omega-3','Omega-6','Proteina','Vit A','Vit C','Vit D','Vit E','Vit K','Thiamin-B1','Riboflavin-B2','Niacin-B3','Vitamina-B6','Folate-B9','Vitamina-B12','Pantothenic acid-B5','Choline-B4','Betaine','Calciu','Fier','Magneziu','Fosfor','Potasiu','Sodiu','Zinc','Cupru','Mangan','Seleniu','Fluor','Cholesterol','Phytosterols']
+Finaldf = Finaldf.set_index(pd.Index(new_index))
+
+def add_dict_values(d):
+    result = {}
+    keys = set().union(*(d[key].keys() for key in d))
+    for key in keys:
+        result[key] = sum(d[k].get(key, 0) for k in d)
+    return result
+
+valori_meniu_d=add_dict_values(final_result)
+valori_meniu=list(valori_meniu_d.values())
+
+def subtract_lists(list1, list2):
+    result = [max(0,float(i) - float(j)*3.5) for i, j in zip(list1, list2)]
+    return result
+
+Valori_ramase=subtract_lists(valori_optime,valori_meniu)
+
+Finaldf['valori ramase dupa portii de 350g']=Valori_ramase
+Finaldf['Valori mediu portii de 350g']=valori_meniu
+Finaldf['Valori mediu portii de 350g']=Finaldf['Valori mediu portii de 350g'].apply(lambda x: float(x) * 3.5)
+Finaldf['Valori Optime']=valori_optime
+st.write(Finaldf)
+
+Valori_normate = [(float(valori_optime[i]) - Valori_ramase[i]) / float(valori_optime[i]) for i in range(len(valori_optime))]
+Valori_ramase_normate=[ Valori_ramase[i] / float(valori_optime[i]) for i in range(len(valori_optime))]
+chartdf = pd.DataFrame({
+    'Procente ramase de consumat':pd.Categorical(new_index, categories=new_index),
+    'max_values':Valori_normate,
+    'Procente': Valori_ramase_normate,
+})
+
+# Create a Categorical datatype for the x-axis
+x_scale = alt.Scale(domain=new_index, zero=False)
+# Define the chart using Altair
+chart = alt.Chart(chartdf).mark_bar().encode(
+    x=alt.X('Procente ramase de consumat:O', scale=x_scale),
+    y='Procente',
+    color=alt.condition(
+        alt.datum.results > alt.datum.max_values,
+        alt.value('red'),  # Set the color to red if the result is greater than the max value
+        alt.value('blue'),  # Set the color to blue if the result is less than or equal to the max value
+    )
+).properties(
+    width=alt.Step(40),  # Set the width of each bar
+)
+
+# Display the chart in Streamlit
+st.altair_chart(chart, use_container_width=True)
+
+#sidebar for categories
+
+# Define columns to display and exclude
+all_columns = new_index
+df.iloc[0, 2:] = new_index
+# set the column names to the values in the first row
+df = df.set_axis(df.iloc[0], axis=1)
+
+# remove the first row from the dataframe
+df = df.iloc[1:]
+# Define sidebar widgets
+sort_col = st.sidebar.selectbox('Selecteaza o categorie', all_columns)
+st.sidebar.write("Optiunea suplimentare, !!!va genera noi retete cu fiecare selectie!!!, foloseste-o separat de optiunea principala pentru a gasi cele mai bogate ingrediente intr-o anumita categorie! (/100g)")
+
+# Filter and sort dataframe
+df[sort_col] = df[sort_col].astype(float)
+display_df = df[[df.columns[0], sort_col]].sort_values(by=sort_col, ascending=False)
+
+# Display dataframe
+st.write(display_df)

creds.json

@@ -0,0 +1,12 @@
+{
+  "type": "service_account",
+  "project_id": "informatii-nutritive",
+  "private_key_id": "8eccc68e0f1978bd8bd9521fd60bda28c012dd48",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\ncTFhPQEc1fG2fD6sen57BRl5\n-----END PRIVATE KEY-----\n",
+  "client_email": "accesinformatiinutritive@informatii-nutritive.iam.gserviceaccount.com",
+  "client_id": "101057535754295785164",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/accesinformatiinutritive%40informatii-nutritive.iam.gserviceaccount.com"
+}

requirements.txt

@@ -0,0 +1,7 @@
+altair==4.2.2
+gspread==5.7.2
+numpy==1.24.2
+pandas==1.3.5
+Pillow==9.4.0
+requests==2.22.0
+streamlit==1.18.1