initial test

app.py

@@ -50,43 +50,231 @@ uploaded_files = st.file_uploader("Upload multiple files", accept_multiple_files
 if uploaded_files is not None:
     
     # with st.spinner('Generating report...'):
-    #     results = get_multiple_predictions(uploaded_file)
+
     for uploaded_file in uploaded_files:
         if uploaded_file.name == 'Flip_accum.xlsx':
             flip_accum1 = pd.read_excel(uploaded_file, skiprows=8, nrows=11, usecols="A:D")
             flip_accum2 = pd.read_excel(uploaded_file, skiprows=24, nrows=5, usecols="A:N")
-            st.write('flip_accum1: ' + str(flip_accum1.shape))  
+            #st.write('flip_accum1: ' + str(flip_accum1.shape))  
         
         elif uploaded_file.name == 'Fold_accum.xlsx':
             fold_accum1 = pd.read_excel(uploaded_file, skiprows=8, nrows=11, usecols="A:D")
             fold_accum2 = pd.read_excel(uploaded_file, skiprows=24, nrows=5, usecols="A:N")
-            st.write('fold_accum1: ' + str(fold_accum1.shape))  
+           #st.write('fold_accum1: ' + str(fold_accum1.shape))  
         
         elif uploaded_file.name == 'Flip_today.xlsx':
             flip_today1 = pd.read_excel(uploaded_file, skiprows=8, nrows=11, usecols="A:D")
             flip_today2 = pd.read_excel(uploaded_file, skiprows=24, nrows=5, usecols="A:M")
-            st.write('flip_today1: ' + str(flip_today1.shape))    
+            #st.write('flip_today1: ' + str(flip_today1.shape))    
 
         elif uploaded_file.name == 'Fold_today.xlsx':
             fold_today1 = pd.read_excel(uploaded_file, skiprows=8, nrows=11, usecols="A:D")
             fold_today2 = pd.read_excel(uploaded_file, skiprows=24, nrows=5, usecols="A:M")
-            st.write('fold_today1: ' + str(fold_today1.shape))   
+            #st.write('fold_today1: ' + str(fold_today1.shape))   
 
         elif uploaded_file.name == 'FlipFold4_accum.xlsx':
             flipfold_accum = pd.read_excel(uploaded_file, skiprows=8, nrows=11, usecols="A:D")
             flipfold_accum2 = pd.read_excel(uploaded_file, skiprows=24, nrows=5, usecols="A:N")
-            st.write('flipfold_accum: ' + str(flipfold_accum.shape))     
+            #st.write('flipfold_accum: ' + str(flipfold_accum.shape))     
 
         elif uploaded_file.name == 'FlipFold4_analysis.xlsx':
             flipfold = pd.read_excel(uploaded_file, skiprows=9)
-            st.write('flipfold: ' + str(flipfold.shape))     
+            #st.write('flipfold: ' + str(flipfold.shape))
+
+        elif uploaded_file.name == 'flipfold4_report_template.docx':
+            doc = DocxTemplate(uploaded_file)     
+
+    if datetime.datetime.now().day == 1:
+        day_suffix = "st"
+    elif datetime.datetime.now().day == 2:
+        day_suffix = "nd"
+    elif datetime.datetime.now().day == 3:
+        day_suffix = "rd"
+    else:
+        day_suffix = "th"
 
+    if round(((flipfold_accum2.iloc[2, 4] - flipfold_accum2.iloc[2, 13])/flipfold_accum2.iloc[2, 13]) * 100) < 0:
+        increase_decrease = "Decrease"
+    else:
+        increase_decrease = "Increase"        
     
-    # st.download_button(
-    #  label="Download report here",
-    #  data=results,
-    #  file_name='results.csv',
-    #  mime='text/csv',
-    #  )
+    flipfold = flipfold[['Symptom\nGroup 1', 'Subsidiary', 'Marketing Name']]
+    flipfold.columns = ['symptom', 'subsidiary', 'Marketing Name']
+
+    display = ['Display', 'Touch', 'OCTA/Backglass Broken', 'Sensor']
+    quick_discharge = ['Quick Discharge', 'Charging', 'Discharging']
+    appearance = ['Appearance', 'Case', 'Button']
+    others = ['In Process', 'WIFI', 'Connection', 'S pen', 'Fault Operation', 'Bluetooth']
+
+    flipfold['symptom'] = flipfold['symptom'].apply(lambda x:
+        'Display' if x in display else
+        'Quick Discharge' if x in quick_discharge else
+        'Appearance' if x in appearance else
+        'Others' if x in others else
+        'Sound/Call Audio' if x == 'Sound/Call audio' else
+        x
+    )
+
+    template = pd.DataFrame({
+        'symptom': ['Total', 'Heat', 'Display', 'Camera', 'Quick Discharge', 'Power', 'Rebooting', 'App/SW', 'Sound/Call Audio', 'Appearance', 'Others'],
+        'SEAO Total': [0]*11,
+        'SAVINA': [0]*11,
+        'SEAU': [0]*11,
+        'SEIN': [0]*11,
+        'SENZ': [0]*11,
+        'SEPCO': [0]*11,
+        'SESP': [0]*11,
+        'SME': [0]*11,
+        'TSE': [0]*11
+    }).set_index('symptom')
+
+    flip4 = flipfold[flipfold['Marketing Name'] == 'Galaxy Z Flip4']
+
+    flip4_groupby = pd.DataFrame(flip4.groupby(['symptom', 'subsidiary'])['subsidiary'].count())
+    flip4_groupby.columns=['count']
+    flip4_groupby.reset_index(inplace=True)
+    flip4_groupby = flip4_groupby.pivot(index='symptom', columns='subsidiary', values='count').fillna(0)
+
+    fold4 = flipfold[flipfold['Marketing Name'] == 'Galaxy Z Fold4']
+
+    fold4_groupby = pd.DataFrame(fold4.groupby(['symptom', 'subsidiary'])['subsidiary'].count())
+    fold4_groupby.columns=['count']
+    fold4_groupby.reset_index(inplace=True)
+    fold4_groupby = fold4_groupby.pivot(index='symptom', columns='subsidiary', values='count').fillna(0)
+
+    template_flip4 = template.copy()
+    template_fold4 = template.copy()
+
+    for col in template.columns:
+        for row in template.index:
+            try:
+                template_flip4.loc[row, col] = flip4_groupby.loc[row, col]
+            except:
+                continue
+                
+    for col in template.columns:
+        for row in template.index:
+            try:
+                template_fold4.loc[row, col] = fold4_groupby.loc[row, col]
+            except:
+                continue
+
+    # Account for SEPCO data entry error
+    template_flip4.loc['Display', 'SEPCO'] = template_flip4.loc['Display', 'SEPCO'] - 4
+    template_flip4.loc['App/SW', 'SEPCO'] = template_flip4.loc['App/SW', 'SEPCO'] - 2
+    template_flip4.loc['Others', 'SEPCO'] = template_flip4.loc['Others', 'SEPCO'] + 6
+
+    # Account for SEVT into SAVINA count
+    template_fold4.loc['Display', 'SAVINA'] = template_fold4.loc['Display', 'SAVINA'] + 5
+    template_fold4.loc['Others', 'SAVINA'] = template_fold4.loc['Others', 'SAVINA'] + 2
+    template_fold4.loc['Rebooting', 'SAVINA'] = template_fold4.loc['Rebooting', 'SAVINA'] + 1
+    template_fold4.loc['Appearance', 'SAVINA'] = template_fold4.loc['Appearance', 'SAVINA'] + 1
+
+    template_flip4.loc['Appearance', 'SAVINA'] = template_flip4.loc['Appearance', 'SAVINA'] + 1
+    template_flip4.loc['Others', 'SAVINA'] = template_flip4.loc['Others', 'SAVINA'] + 2
+
+    template_flip4['SEAO Total'] = template_flip4[['SAVINA', 'SEAU', 'SEIN', 'SENZ', 'SEPCO', 'SESP', 'SME', 'TSE']].sum(axis=1)
+    template_flip4.loc['Total'] = template_flip4[['SEAO Total', 'SAVINA', 'SEAU', 'SEIN', 'SENZ', 'SEPCO', 'SESP', 'SME', 'TSE']].sum(axis=0)
+    template_flip4 = template_flip4.astype(int)
+
+    template_fold4['SEAO Total'] = template_fold4[['SEAO Total', 'SAVINA', 'SEAU', 'SEIN', 'SENZ', 'SEPCO', 'SESP', 'SME', 'TSE']].sum(axis=1)
+    template_fold4.loc['Total'] = template_fold4[['SEAO Total', 'SAVINA', 'SEAU', 'SEIN', 'SENZ', 'SEPCO', 'SESP', 'SME', 'TSE']].sum(axis=0)
+    template_fold4 = template_fold4.astype(int)
+
+    flip4_dict = {'a' + str(i):  template_flip4.values.flatten()[i-1] for i in range(1,100)}
+    fold4_dict = {'b' + str(i):  template_fold4.values.flatten()[i-1] for i in range(1,100)}
+
+    context = {
+    
+    #"topleft": topleft,
+    #"topright": topright,
+    #"bottomleft": bottomleft,
+    #"bottomright": bottomright,
+    
+    "date0" : int((datetime.date.today() - date(2022, 9, 2))/ timedelta(days=1) + 1),
+    "date1" : datetime.datetime.now().strftime("%#d.%#m.%Y"),
+    "date2" : (datetime.datetime.now() - timedelta(days=1)).strftime("%#d/%#m"),
+    "date3": datetime.datetime.now().strftime("%b.%#d"),
+    "day_suffix": day_suffix,
+    "v2": "{:>6}".format(f'{flip_today1.iloc[5, 2] + fold_today1.iloc[5, 2]:,}'),
+    "v3": f'{int(flip_accum1.iloc[3, 2]):,}',
+    "v4": f'{int(flip_accum1.iloc[5, 2]):,}',
+    "v5": f'{int(flip_accum1.iloc[7, 2]):,}',
+    
+    "v6": f'{int(flip_accum2.iloc[1, 12]):,}',
+    "v7": f'{int(flip_accum2.iloc[1, 5]):,}',
+    "v8": f'{int(flip_accum2.iloc[1, 6]):,}',
+    
+    "v9": f'{int(fold_accum1.iloc[3, 2]):,}',
+    "v10": f'{int(fold_accum1.iloc[5, 2]):,}',
+    "v11": f'{int(fold_accum1.iloc[7, 2]):,}',
+    
+    "v12": f'{int(fold_accum2.iloc[1, 12]):,}',
+    "v13": f'{int(fold_accum2.iloc[1, 5]):,}',
+    "v14": f'{int(fold_accum2.iloc[1, 6]):,}',
+    
+#     "v21": f'{int(flip_today1.iloc[3, 2]):,}', # changed on 5 Sep 2022
+#     "v22": f'{int(flip_today1.iloc[5, 2]):,}',
+#     "v23": f'{int(flip_today1.iloc[7, 2]):,}',
+    
+    "v21": f'{int(flip_today2.iloc[1, 12]):,}',
+    "v22": f'{int(flip_today2.iloc[1, 5]):,}',
+    "v23": f'{int(flip_today2.iloc[1, 6]):,}',
+    
+    "v24": f'{int(fold_today2.iloc[1, 12]):,}',
+    "v25": f'{int(fold_today2.iloc[1, 5]):,}',
+    "v26": f'{int(fold_today2.iloc[1, 6]):,}',
+    
+    # Table 1 Subtotals
+    "v16": f'{int(flip_accum1.iloc[7, 2] + fold_accum1.iloc[7, 2]):,}',
+    "v17": f'{int(flip_accum1.iloc[3, 2] + fold_accum1.iloc[3, 2]):,}',
+    "v18": f'{int(flip_accum2.iloc[1, 12] + fold_accum2.iloc[1, 12]):,}',
+    "v19": f'{int(flip_accum2.iloc[1, 5] + fold_accum2.iloc[1, 5]):,}',
+    "v20": f'{int(flip_accum2.iloc[1, 6] + fold_accum2.iloc[1, 6]):,}',
+    "v27": f'{int(flip_today2.iloc[1, 12]) + int(fold_today2.iloc[1, 12]):,}',
+    "v28": f'{int(flip_today2.iloc[1, 5]) + int(fold_today2.iloc[1, 5]):,}',
+    "v29": f'{int(flip_today2.iloc[1, 6]) + int(fold_today2.iloc[1, 6]):,}',
+    "v30": f'{int(flip_today2.iloc[1, 4]):,}',
+    "v31": f'{int(fold_today2.iloc[1, 4]):,}',
+    "v15": f'{int(flip_today2.iloc[1, 4]) + int(fold_today2.iloc[1, 4]):,}',
+    "v1": f'{int(flip_accum1.iloc[5, 2] + fold_accum1.iloc[5, 2]):,}',
+    
+    "v32": f'{int(flipfold_accum2.iloc[2, 4]):,}',
+    "v33": f'{int(flip_accum2.iloc[2, 4]):,}',
+    "v34": f'{int(fold_accum2.iloc[2, 4]):,}',
+    
+    "v35": f'{int(flipfold_accum2.iloc[2, 13]):,}',
+    "v36": f'{int(fold_accum2.iloc[2, 13]):,}',
+    "v37": f'{int(flip_accum2.iloc[2, 13]):,}',
+    "v38": abs(round(((flipfold_accum2.iloc[2, 4] - flipfold_accum2.iloc[2, 13])/flipfold_accum2.iloc[2, 13]) * 100)),
+    "increase_decrease": increase_decrease,
+    
+    "c12": int(template_flip4.loc[['Heat', 'Quick Discharge', 'Power', 'Rebooting'], 'SEAO Total'].sum()),
+    "c19": int(template_fold4.loc[['Heat', 'Quick Discharge', 'Power', 'Rebooting'], 'SEAO Total'].sum()),
+    "c14": int(template_flip4.loc[['Camera', 'Others'], 'SEAO Total'].sum()),
+    "c21": int(template_fold4.loc[['Camera', 'Others'], 'SEAO Total'].sum()),
+    "c1": int(flip4_dict["a1"] + fold4_dict["b1"]),
+    "c2": int(flip4_dict["a19"] + fold4_dict["b19"]),
+    "c3": int(flip4_dict["a64"] + fold4_dict["b64"]),
+    "c4": int(flip4_dict["a82"] + fold4_dict["b82"]),
+    "c5": int(template_flip4.loc[['Heat', 'Quick Discharge', 'Power', 'Rebooting'], 'SEAO Total'].sum() + template_fold4.loc[['Heat', 'Quick Discharge', 'Power', 'Rebooting'], 'SEAO Total'].sum()),
+    "c6": int(flip4_dict["a73"] + fold4_dict["b73"]),
+    "c7": int(template_flip4.loc[['Camera', 'Others'], 'SEAO Total'].sum() + template_fold4.loc[['Camera', 'Others'], 'SEAO Total'].sum()),
+    
+    "d1": round(100*(flip4_dict["a19"] + fold4_dict["b19"])/(flip4_dict["a1"] + fold4_dict["b1"])),
+    "d2": round(100*(flip4_dict["a64"] + fold4_dict["b64"])/(flip4_dict["a1"] + fold4_dict["b1"])),
+    "d3": round(100*(template_flip4.loc[['Heat', 'Quick Discharge', 'Power', 'Rebooting'], 'SEAO Total'].sum() + template_fold4.loc[['Heat', 'Quick Discharge', 'Power', 'Rebooting'], 'SEAO Total'].sum())/(flip4_dict["a1"] + fold4_dict["b1"]))
+    }
+
+    context2 = {**context, **flip4_dict, **fold4_dict}
+    doc.render(context2)
+    results = doc.save()
+
+    st.download_button(
+     label="Download report here",
+     data=results,
+     file_name="SEAO Fold 4_Flip 4 Quality Monitoring (" + datetime.datetime.now().strftime("%#d %b") + ").docx",
+     mime='application/vnd.openxmlformats-officedocument.wordprocessingml.document',
+     )