Update app.py

app.py

@@ -128,117 +128,3 @@ if __name__ == "__main__":
     app = DataAutomationApp()
     app.run()
 
-'''
-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!")
-
-                # 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)
-
-                
-
-                # 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)
-
-               
-
-                # 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()
-
-'''