Update app.py

app.py

@@ -1,101 +1,121 @@
 import streamlit as st
+from data_cleaning import DataCleaner
+from visualization import VisualizationSelector
+from data_analysis import DataAnalyzer
+from report_generator import ReportGenerator
+from api_integration import APIConnector
+from natural_language_query import NLQueryEngine
+from predictive_analytics import PredictiveAnalytics
+from anomaly_detection import AnomalyDetector
+from time_series_forecasting import TimeSeriesForecaster
+from geospatial_visualization import GeospatialVisualizer
+from sentiment_analysis import SentimentAnalyzer
+from data_storytelling import DataStoryteller
+from data_privacy import DataPrivacyTool
 import pandas as pd
-from data_processor import DataProcessor
-from visualizer import Visualizer
-from analyzer import Analyzer
-from openai_agent import OpenAIAgent
-from time_series_analyzer import TimeSeriesAnalyzer
-from machine_learning import MachineLearning
-from text_analyzer import TextAnalyzer
-from geospatial_analyzer import GeospatialAnalyzer
-from utils import load_data, save_data
-
-st.set_page_config(page_title="AI-Powered Data Analysis", layout="wide")
-
-st.title("AI-Powered Data Analysis and Visualization")
-
-# File uploader
-uploaded_file = st.sidebar.file_uploader("Choose a CSV file", type="csv")
-
-if uploaded_file is not None:
-    df = load_data(uploaded_file)
-    
-    # Initialize components
-    data_processor = DataProcessor(df)
-    visualizer = Visualizer()
-    analyzer = Analyzer()
-    openai_agent = OpenAIAgent()
-    time_series_analyzer = TimeSeriesAnalyzer()
-    ml = MachineLearning()
-    text_analyzer = TextAnalyzer()
-    geo_analyzer = GeospatialAnalyzer()
-
-    # Sidebar for feature selection
-    feature = st.sidebar.radio(
-        "Select a feature",
-        ["Data Overview", "Data Cleaning", "Data Visualization", "Data Analysis", 
-         "AI Insights", "Time Series Analysis", "Machine Learning", 
-         "Text Analysis", "Geospatial Analysis", "Custom AI Tasks"]
-    )
-
-    if feature == "Data Overview":
-        st.header("Data Overview")
-        st.write("Dataset shape:", df.shape)
-        st.write("Columns:", df.columns.tolist())
-        st.write("Sample data:")
-        st.write(df.head())
-        
-        if st.checkbox("Show data types"):
-            st.write(df.dtypes)
-        
-        if st.checkbox("Show summary statistics"):
-            st.write(df.describe())
-
-    elif feature == "Data Cleaning":
-        st.header("Data Cleaning and Preprocessing")
-        df_cleaned = data_processor.clean_data()
-        st.write("Cleaned data shape:", df_cleaned.shape)
-        st.write(df_cleaned.head())
-        
-        if st.button("Save Cleaned Data"):
-            save_data(df_cleaned, "cleaned_data.csv")
-            st.success("Cleaned data saved successfully!")
-
-    elif feature == "Data Visualization":
-        st.header("Data Visualization")
-        visualizer.create_visualizations(df)
-
-    elif feature == "Data Analysis":
-        st.header("Data Analysis")
-        analyzer.perform_analysis(df)
-
-    elif feature == "AI Insights":
-        st.header("AI-Powered Insights")
-        openai_agent.generate_insights(df)
-
-    elif feature == "Time Series Analysis":
-        st.header("Time Series Analysis")
-        time_series_analyzer.analyze(df)
-
-    elif feature == "Machine Learning":
-        st.header("Machine Learning")
-        ml.perform_ml_tasks(df)
-
-    elif feature == "Text Analysis":
-        st.header("Text Analysis")
-        text_analyzer.analyze_text(df)
-
-    elif feature == "Geospatial Analysis":
-        st.header("Geospatial Analysis")
-        geo_analyzer.analyze_geospatial_data(df)
-
-    elif feature == "Custom AI Tasks":
-        st.header("Custom AI Tasks")
-        openai_agent.custom_ai_task(df)
-
-else:
-    st.write("Please upload a CSV file to get started.")
-
-# Add a footer
-st.sidebar.markdown("---")
-st.sidebar.info("Developed by Your Company Name")
+
+class DataAutomationApp:
+    def __init__(self):
+        self.data = None
+        self.cleaner = DataCleaner()
+        self.visualizer = VisualizationSelector()
+        self.analyzer = DataAnalyzer()
+        self.report_generator = ReportGenerator()
+        self.api_connector = APIConnector()
+        self.nl_query_engine = NLQueryEngine()
+        self.predictive_analytics = PredictiveAnalytics()
+        self.anomaly_detector = AnomalyDetector()
+        self.time_series_forecaster = TimeSeriesForecaster()
+        self.geospatial_visualizer = GeospatialVisualizer()
+        self.sentiment_analyzer = SentimentAnalyzer()
+        self.data_storyteller = DataStoryteller()
+        self.data_privacy_tool = DataPrivacyTool()
+
+    def load_data(self, file):
+        if file.name.endswith('.csv'):
+            self.data = pd.read_csv(file)
+        elif file.name.endswith(('.xls', '.xlsx')):
+            self.data = pd.read_excel(file)
+        else:
+            st.error("Unsupported file format. Please upload a CSV or Excel file.")
+
+    def run(self):
+        st.title("Data Automation and Visualization App")
+
+        # File upload
+        uploaded_file = st.file_uploader("Choose a CSV or Excel file", type=["csv", "xlsx"])
+        if uploaded_file is not None:
+            self.load_data(uploaded_file)
+            if self.data is not None:
+                st.success("Data loaded successfully!")
+
+                # Data cleaning
+                if st.button("Clean Data"):
+                    self.data = self.cleaner.clean(self.data)
+                    st.write(self.data.head())
+
+                # Visualization
+                if st.button("Generate Visualizations"):
+                    visualizations = self.visualizer.select_visualizations(self.data)
+                    for viz in visualizations:
+                        st.pyplot(viz)
+
+                # Data Analysis
+                if st.button("Analyze Data"):
+                    insights = self.analyzer.analyze(self.data)
+                    st.write(insights)
+
+                # Natural Language Query
+                query = st.text_input("Ask a question about your data:")
+                if query:
+                    result = self.nl_query_engine.process_query(query, self.data)
+                    st.write(result)
+
+                # Predictive Analytics
+                if st.button("Run Predictive Analytics"):
+                    prediction = self.predictive_analytics.predict(self.data)
+                    st.write(prediction)
+
+                # Anomaly Detection
+                if st.button("Detect Anomalies"):
+                    anomalies = self.anomaly_detector.detect(self.data)
+                    st.write(anomalies)
+
+                # Time Series Forecasting
+                if st.button("Forecast Time Series"):
+                    forecast = self.time_series_forecaster.forecast(self.data)
+                    st.write(forecast)
+
+                # Geospatial Visualization
+                if st.button("Geospatial Visualization"):
+                    geo_viz = self.geospatial_visualizer.visualize(self.data)
+                    st.pyplot(geo_viz)
+
+                # Sentiment Analysis
+                if st.button("Analyze Sentiment"):
+                    sentiment = self.sentiment_analyzer.analyze(self.data)
+                    st.write(sentiment)
+
+                # Data Storytelling
+                if st.button("Generate Data Story"):
+                    story = self.data_storyteller.generate_story(self.data)
+                    st.write(story)
+
+                # Data Privacy Check
+                if st.button("Check Data Privacy"):
+                    privacy_report = self.data_privacy_tool.check_privacy(self.data)
+                    st.write(privacy_report)
+
+                # Report Generation
+                if st.button("Generate Report"):
+                    report = self.report_generator.generate(self.data)
+                    st.download_button(
+                        label="Download Report",
+                        data=report,
+                        file_name="data_report.txt",
+                        mime="text/plain"
+                    )
+
+if __name__ == "__main__":
+    app = DataAutomationApp()
+    app.run()