Update app.py

app.py

@@ -12,6 +12,137 @@ 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()
+
+'''
+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
@@ -108,4 +239,6 @@ class DataAutomationApp:
 
 if __name__ == "__main__":
     app = DataAutomationApp()
-    app.run()
+    app.run()
+
+'''