Merge pull request #24 from dncq/dev/model

add: data and prepare predict model

src/app/app.py

@@ -1,27 +1,17 @@
 import streamlit as st
 
 laptop_config = {
-    'Company': ['Apple', 'Best Notebooks', 'Microsoft', 'GIGABYTE', 'Panasonic', 'Samsung', 'LG', 'MSI',
- 'Asus', 'Acer', 'HP', 'Lenovo',
- 'Dell'],
+    'Brand': ['Dell','Apple', 'Lenovo', 'HP', 'Asus', 'Acer', 'MSI', 'Microsoft', 'Other'],
     'CPU': ['i5', 'i7', 'Ryzen 5', 'Ryzen 7'],
     'RAM': ['8GB', '16GB', '32GB'],
     'GPU': ['Integrated', 'NVIDIA GTX', 'NVIDIA RTX', 'AMD Radeon'],
     'Storage': ['256GB SSD', '512GB SSD', '1TB HDD', '1TB SSD']
 }
 
-st.title('Are You Sure Prediction')
-
-st.button("Button thoi :)))")
+st.title('Laptop Price Prediction')
 
 st.caption("#fromhelaricawithluv")
 
-st.checkbox("I am sure vailon!")
-
-st.code('''def answer_tqk(question: str)
-    if question == "Are you sure?":
-        print("I am sure vailon")''')
-
 company = st.selectbox('Brand', laptop_config['Company'])
 cpu = st.selectbox('CPU', laptop_config['CPU'])
 # ram = st.selectbox('RAM', laptop_config['RAM'])

src/app/predict.py

@@ -0,0 +1,46 @@
+import pickle as pk
+import pandas as pd
+from mapping import *
+
+def predict(model_name: pk.Pickler, 
+            brand: str, 
+            cpu_name: str, 
+            gpu_name: str,
+            monitor: str,
+            monitor_size: str,
+            ram: str,
+            storage: str,
+            os: str,
+            weight: str) -> float:
+
+    PARENT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
+    PARENT_PATH = os.path.abspath(os.path.join(PARENT_PATH, '..'))
+    DATA_PATH = os.path.abspath(os.path.join(PARENT_PATH, "data", "raw", "cpu_gpu_mark"))
+
+    CPU_FILENAME = "cpu_mark.csv"
+    GPU_FILENAME = "gpu_mark.csv"
+
+    cpu_df = pd.read_csv(os.path.abspath(os.path.join(DATA_PATH, CPU_FILENAME)), index_col = 0)
+    gpu_df = pd.read_csv(os.path.abspath(os.path.join(DATA_PATH, GPU_FILENAME)), index_col = 0)
+
+    cpu_name_list = [cpu_df['CPU Name'][idx].lower() for idx in cpu_df.index]
+    gpu_name_list = [gpu_df['GPU Name'][idx].lower() for idx in gpu_df.index]
+
+    # Transform data to predict
+    name_cpu, cpu_mark = get_cpu_name(cpu_name)
+    name_gpu, gpu_mark = get_gpu_name(gpu_name)
+
+    # Monitor to Float
+    if "\"" in monitor or "\'" in monitor:
+        elements = monitor.split()
+        if len(elements) == 2:
+            pros_monitor, _ = elements[0], elements[1]
+            pros_monitor = float(pros_monitor[:-1])
+
+        if len(elements) == 1:
+            monitor = elements[0]
+            pros_monitor = float(pros_monitor[:-1])
+    else:
+        pros_monitor = float(monitor[:-1])
+
+