Update app.py

app.py

@@ -1,14 +1,17 @@
-import os
 import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
 from pandasai import SmartDataframe
-from pandasai.callbacks import BaseCallback
 from pandasai.llm import OpenAI
+from pandasai.callbacks import BaseCallback
 from pandasai.responses.response_parser import ResponseParser
-from data import load_data
+import os
 
-# Fungsi untuk mendapatkan daftar folder dalam direktori
-def list_folders(directory):
-    return [name for name in os.listdir(directory) if os.path.isdir(os.path.join(directory, name))]
+# Set OpenAI API Token
+OPENAI_API_TOKEN = os.getenv("BIT_OPENAI_API_KEY", "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")
+
+# Load OpenAI Mini model
+llm = OpenAI(api_token=OPENAI_API_TOKEN, model_name="gpt-4o-mini")
 
 class StreamlitCallback(BaseCallback):
     def __init__(self, container) -> None:
@@ -34,48 +37,59 @@ class StreamlitResponse(ResponseParser):
         st.write(result["value"])
         return
 
-# Tampilkan judul aplikasi
-st.write("# Chat with Tabular Dataframe 🦙")
-
-try:
-    from typing_extensions import TypedDict  # ✅ Correct for Python <3.12
-except ImportError:
-    from typing import TypedDict  # ✅ Works for Python 3.12+
-
-
-# Menentukan direktori asal
-root_dir = "./data"
-
-# Mendapatkan daftar folder
-folders = list_folders(root_dir)
-
-# Menampilkan kotak pemilihan folder
-selected_folder = st.selectbox("Pilih folder data", folders)
-
-# Memuat data berdasarkan folder yang dipilih
-if selected_folder:
-    df = load_data(os.path.join(root_dir, selected_folder))
-
-    # Menampilkan preview data
-    with st.expander("🔎 Dataframe Preview"):
-        st.write(df.tail(3))
-
-    # Membuat input untuk query
-    query = st.text_area("🗣️ Chat with Dataframe")
-    container = st.container()
-
-    # Eksekusi query jika ada input
-    if query:
-        llm = OpenAI(api_token=os.environ["BIT_OPENAI_API_KEY"])
-        query_engine = SmartDataframe(
-            df,
-            config={
+def process_file(file):
+    file_extension = file.name.split(".")[-1].lower()
+    try:
+        if file_extension == "csv":
+            # Detect if semicolon or comma is used as a separator
+            first_line = file.readline().decode("utf-8")
+            file.seek(0)  # Reset file pointer
+            delimiter = ";" if ";" in first_line else ","
+            df = pd.read_csv(file, delimiter=delimiter)
+        elif file_extension in ["xls", "xlsx"]:
+            import openpyxl  # Ensures openpyxl is available
+            df = pd.read_excel(file, engine="openpyxl")
+        else:
+            st.error("Unsupported file format. Please upload a CSV or Excel file.")
+            return None, None
+    except ImportError as e:
+        st.error(f"Error: {e}. Please install 'openpyxl' for Excel support.")
+        return None, None
+    
+    sdf = SmartDataframe(df, config={
                 "llm": llm,
-                "response_parser": StreamlitResponse,
-                # "callback": StreamlitCallback(container),
-            },
-        )
-
-        answer = query_engine.chat(query)
-else:
-    st.write("Silakan pilih folder untuk memuat data.")
+                "save_logs": True,
+                "verbose": False,
+                "response_parser": StreamlitResponse
+                })
+    return df, sdf
+
+def main():
+    st.title("AI-Powered Dataframe Analysis with OpenAI")
+    uploaded_file = st.file_uploader("Upload CSV or Excel File", type=["csv", "xls", "xlsx"])
+    
+    if uploaded_file:
+        df, sdf = process_file(uploaded_file)
+        if df is not None:
+            st.subheader("🔎 Data Preview")
+            
+            with st.expander("View Data Summary"):
+                st.write("### Data Overview")
+                st.dataframe(df.describe(include='all'))
+                
+                st.write("### First 10 Rows of Data")
+                st.dataframe(df.head(10))
+                
+                st.write("### Column Details")
+                for col in df.columns:
+                    st.write(f"**{col}**")
+                    st.dataframe(df[[col]].head(10))
+            
+            query = st.text_area("Enter Your Query")
+            
+            if query:
+                container = st.container()
+                answer = sdf.chat(query)
+
+if __name__ == "__main__":
+    main()