app.py

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 sentiment_analysis import SentimentAnalyzer
from data_storytelling import DataStoryteller
import pandas as pd

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.sentiment_analyzer = SentimentAnalyzer()
        self.data_storyteller = DataStoryteller()

    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!")

                # Sidebar for feature selection
                st.sidebar.title("Select a Feature")
                feature = st.sidebar.radio(
                    "Choose what you'd like to do:", 
                    ("Clean Data", "Generate Visualizations", "Analyze Data", 
                     "Natural Language Query", "Run Predictive Analytics", 
                     "Detect Anomalies", "Forecast Time Series", 
                     "Analyze Sentiment", "Generate Data Story", 
                     "Generate Report")
                )

                # Based on the selected feature, execute corresponding functionality
                if feature == "Clean Data":
                    st.subheader("Clean Data")
                    if st.button("Clean Data"):
                        self.data = self.cleaner.clean(self.data)
                        st.write(self.data.head())

                elif feature == "Generate Visualizations":
                    st.subheader("Generate Visualizations")
                    if st.button("Generate Visualizations"):
                        visualizations = self.visualizer.select_visualizations(self.data)
                        for viz in visualizations:
                            st.pyplot(viz)

                elif feature == "Analyze Data":
                    st.subheader("Analyze Data")
                    if st.button("Analyze Data"):
                        insights = self.analyzer.analyze(self.data)
                        st.write(insights)

                elif feature == "Natural Language Query":
                    st.subheader("Ask a Question About Your Data")
                    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)

                elif feature == "Run Predictive Analytics":
                    st.subheader("Run Predictive Analytics")
                    if st.button("Run Predictive Analytics"):
                        prediction = self.predictive_analytics.predict(self.data)
                        st.write(prediction)

                elif feature == "Detect Anomalies":
                    st.subheader("Detect Anomalies")
                    if st.button("Detect Anomalies"):
                        anomalies = self.anomaly_detector.detect(self.data)
                        st.write(anomalies)

                elif feature == "Forecast Time Series":
                    st.subheader("Forecast Time Series")
                    if st.button("Forecast Time Series"):
                        forecast = self.time_series_forecaster.forecast(self.data)
                        st.write(forecast)

                elif feature == "Analyze Sentiment":
                    st.subheader("Analyze Sentiment")
                    if st.button("Analyze Sentiment"):
                        sentiment = self.sentiment_analyzer.analyze(self.data)
                        st.write(sentiment)

                elif feature == "Generate Data Story":
                    st.subheader("Generate Data Story")
                    if st.button("Generate Data Story"):
                        story = self.data_storyteller.generate_story(self.data)
                        st.write(story)

                elif feature == "Generate Report":
                    st.subheader("Generate Report")
                    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()