add: prepare to merge

src/app/app.py

@@ -0,0 +1,56 @@
+import streamlit as st
+
+laptop_config = {
+    'Company': ['Apple', 'Best Notebooks', 'Microsoft', 'GIGABYTE', 'Panasonic', 'Samsung', 'LG', 'MSI',
+ 'Asus', 'Acer', 'HP', 'Lenovo',
+ 'Dell'],
+    '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.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'])
+
+ram = st.select_slider("RAM", options=['4GB', '8GB', '12GB', '16GB', '32GB', '64GB'])
+gpu = st.selectbox('GPU', laptop_config['GPU'])
+storage = st.selectbox('Storage', laptop_config['Storage'])
+
+def predict_price(company, cpu, ram, gpu, storage):
+
+    return 1500000000
+
+
+if st.button('Prediction'):
+    predicted_price = predict_price(company, cpu, ram, gpu, storage)
+
+    st.success(f'{predicted_price} USD')
+
+    st.camera_input('Camera nek')
+
+st.download_button("Download Button", data="main.py")
+st.code('''st.download_button("Download Button", data="str")
+# data la string, khi download ve se thanh file txt chua doan string do''')
+
+with st.form("This is form"):
+    st.write("Ben trong form nek")
+
+    st.slider("Slider nek", 15, 20)
+
+    st.checkbox("Checkbox nek")
+
+    st.form_submit_button("Submit")

src/app/mapping.py

@@ -3,12 +3,15 @@
 import pandas as pd
 from fuzzywuzzy import fuzz
 
-pd.set_option("display.max_rows", None)
-path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
-sys.path.insert(0, path)
+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_df = pd.read_csv(r"C:\python code\Intro to Data Science\laptop-prices-analysis\Data\Raw\Mark\cpu_mark.csv", index_col = 0)
-gpu_df = pd.read_csv(r'C:\python code\Intro to Data Science\laptop-prices-analysis\Data\Raw\Mark\gpu_mark.csv', index_col = 0)
+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]
@@ -23,18 +26,22 @@ def mapping(s, slist):
             found[1] = i
             break
         acc=sum([fuzz.ratio(slist[i], s2),fuzz.partial_ratio(slist[i], s2),fuzz.token_sort_ratio(slist[i], s2),fuzz.token_set_ratio(slist[i], s2)])/4
-#         acc=max(fuzz.ratio(slist[i], s2),fuzz.partial_ratio(slist[i], s2),fuzz.token_sort_ratio(slist[i], s2),fuzz.token_set_ratio(slist[i], s2))
+
         if found[0]<acc:
             found[0] = acc
             found[1] = i
     return found
 
 def get_cpu_name(cpu):
     acc , pos = mapping(cpu,cpu_name_list)
-    return cpu_df['CPU Name'][pos]
+    return cpu_df['CPU Name'][pos], cpu_df['CPU Rank'][pos]
 
 def get_gpu_name(gpu):
     acc, pos = mapping(gpu,gpu_name_list)
-    return gpu_df['GPU Name'][pos]
+    return gpu_df['GPU Name'][pos], gpu_df['GPU Rank'][pos]
+
+if __name__ == '__main__':
+    TEST_CPU_NAME = 'Intel core i5 1155g7'
 
-print(get_cpu_name('Intel core i5 1155g7'))
+    _, cpu_mark = get_cpu_name(TEST_CPU_NAME)
+    print(cpu_mark)