Implement prediction model

.gitignore

@@ -4,4 +4,10 @@
 *.DS_Store
 *.ipynb_checkpoints
 data/
-test.py
+test.py
+.venv
+.history
+
+.env
+.vscode
+.conda

app/app.py

@@ -1,109 +1,87 @@
 import time
+from data_preprocessing import preprocess_pipeline
 import streamlit as st
 from streamlit_lottie import st_lottie
+from src.data_preprocess.pipeline_selector import get_selected_pipeline
 from util import load_lottie, stream_data, welcome_message, introduction_message
 from prediction_model import prediction_model_pipeline
 from cluster_model import cluster_model_pipeline
 from regression_model import regression_model_pipeline
-from visualization import data_visualization
+from src.visualization import data_visualization, preprocessing
 from src.util import read_file_from_streamlit
 
-st.set_page_config(page_title="Streamline Analyst", page_icon=":rocket:", layout="wide")
-
-# TITLE SECTION
-with st.container():
-    st.subheader("Hello there 👋")
-    st.title("Welcome to Streamline Analyst!")
-    if 'initialized' not in st.session_state:
-        st.session_state.initialized = True
-    if st.session_state.initialized:
-        st.session_state.welcome_message = welcome_message()
-        st.write(stream_data(st.session_state.welcome_message))
-        time.sleep(0.5)
-        st.write("[Github > ](https://github.com/Wilson-ZheLin/Streamline-Analyst)")
-        st.session_state.initialized = False
-    else:
-        st.write(st.session_state.welcome_message)
-        st.write("[Github > ](https://github.com/Wilson-ZheLin/Streamline-Analyst)")
-
-# INTRO SECTION
-with st.container():
-    st.divider()
-    if 'lottie' not in st.session_state:
-        st.session_state.lottie_url1, st.session_state.lottie_url2 = load_lottie()
-        st.session_state.lottie = True
+import os
+from dotenv import load_dotenv
 
-    left_column_r1, right_column_r1 = st.columns([6, 4])
-    with left_column_r1:
-        st.header("What can Streamline Analyst do?")
-        st.write(introduction_message()[0])
-    with right_column_r1:
-        if st.session_state.lottie:
-            st_lottie(st.session_state.lottie_url1, height=280, key="animation1")
+# Load environment variables from .env file
+load_dotenv()
 
-    left_column_r2, _, right_column_r2 = st.columns([6, 1, 5])
-    with left_column_r2:
-        if st.session_state.lottie:
-            st_lottie(st.session_state.lottie_url2, height=200, key="animation2")
-    with right_column_r2:
-        st.header("Simple to Use")
-        st.write(introduction_message()[1])
+st.set_page_config(page_title="Streamline Analyst", page_icon=":rocket:", layout="wide")
+API_KEY = os.getenv("OPENAI_KEY")
 
 # MAIN SECTION
 with st.container():
     st.divider()
     st.header("Let's Get Started")
     left_column, right_column = st.columns([6, 4])
     with left_column:
-        API_KEY = st.text_input(
-            "Your API Key won't be stored or shared!",
-            placeholder="Enter your API key here...",
+        QUESTION = st.text_input(
+            "Nhập câu hỏi phân tích của bạn",
+            placeholder="...",
+            value="Visualiza giá nhà theo quận dùng heatmap",
+        )
+        uploaded_file = st.file_uploader(
+            "Choose a data file. Your data won't be stored as well!",
+            accept_multiple_files=False,
+            type=["csv", "json", "xls", "xlsx"],
         )
-        st.write("👆Your OpenAI API key:")
-        uploaded_file = st.file_uploader("Choose a data file. Your data won't be stored as well!", accept_multiple_files=False, type=['csv', 'json', 'xls', 'xlsx'])
         if uploaded_file:
             if uploaded_file.getvalue():
                 uploaded_file.seek(0)
                 st.session_state.DF_uploaded = read_file_from_streamlit(uploaded_file)
                 st.session_state.is_file_empty = False
             else:
                 st.session_state.is_file_empty = True
-
-    with right_column:
-        SELECTED_MODEL = st.selectbox(
-        'Which OpenAI model do you want to use?',
-        ('GPT-4-Turbo', 'GPT-3.5-Turbo'))
 
-        MODE = st.selectbox(
-        'Select proper data analysis mode',
-        ('Predictive Classification', 'Clustering Model', 'Regression Model', 'Data Visualization'))
-
-        st.write(f'Model selected: :green[{SELECTED_MODEL}]')
-        st.write(f'Data analysis mode: :green[{MODE}]')
+    with right_column:
+        SELECTED_MODEL = st.selectbox("Which OpenAI model do you want to use?", ("GPT-4o-mini", "GPT-4o"))
 
     # Proceed Button
-    is_proceed_enabled = uploaded_file is not None and API_KEY != "" or uploaded_file is not None and MODE == "Data Visualization"
+    is_proceed_enabled = (
+        uploaded_file is not None and API_KEY != "" or uploaded_file is not None and MODE == "Data Visualization"
+    )
 
     # Initialize the 'button_clicked' state
-    if 'button_clicked' not in st.session_state:
+    if "button_clicked" not in st.session_state:
         st.session_state.button_clicked = False
-    if st.button('Start Analysis', disabled=(not is_proceed_enabled) or st.session_state.button_clicked, type="primary"):
+    if st.button(
+        "Start Analysis", disabled=(not is_proceed_enabled) or st.session_state.button_clicked, type="primary"
+    ):
         st.session_state.button_clicked = True
     if "is_file_empty" in st.session_state and st.session_state.is_file_empty:
-        st.caption('Your data file is empty!')
+        st.caption("Your data file is empty!")
 
     # Start Analysis
     if st.session_state.button_clicked:
-        GPT_MODEL = 4 if SELECTED_MODEL == 'GPT-4-Turbo' else 3.5
+        GPT_MODEL = 4 if SELECTED_MODEL == "GPT-4-Turbo" else "GPT-4o-mini"
         with st.container():
             if "DF_uploaded" not in st.session_state:
                 st.error("File is empty!")
             else:
-                if MODE == 'Predictive Classification':
-                    prediction_model_pipeline(st.session_state.DF_uploaded, API_KEY, GPT_MODEL)
-                elif MODE == 'Clustering Model':
-                    cluster_model_pipeline(st.session_state.DF_uploaded, API_KEY, GPT_MODEL)
-                elif MODE == 'Regression Model':
-                    regression_model_pipeline(st.session_state.DF_uploaded, API_KEY, GPT_MODEL)
-                elif MODE == 'Data Visualization':
-                    data_visualization(st.session_state.DF_uploaded)
+                # Select pipeline to process
+                MODE = get_selected_pipeline(st.session_state.DF_uploaded, QUESTION, GPT_MODEL, API_KEY)
+                st.write(f"Model selected: :green[{SELECTED_MODEL}]")
+                st.write(f"Data analysis mode: :green[{MODE}]")
+
+                # Start preprocessing pipeline
+                preprocess_pipeline(st.session_state.DF_uploaded, API_KEY, GPT_MODEL, QUESTION)
+
+                # Start selected pipeline
+                if MODE == "Predictive Classification":
+                    prediction_model_pipeline(st.session_state.DF_uploaded, API_KEY, GPT_MODEL, QUESTION)
+                elif MODE == "Clustering Model":
+                    cluster_model_pipeline(st.session_state.DF_uploaded, API_KEY, GPT_MODEL, QUESTION)
+                elif MODE == "Regression Model":
+                    regression_model_pipeline(st.session_state.DF_uploaded, API_KEY, GPT_MODEL, QUESTION)
+                elif MODE == "Data Visualization":
+                    data_visualization(st.session_state.DF_uploaded, API_KEY, GPT_MODEL, QUESTION)