Create app.py
Browse files
app.py
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import streamlit as st
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import pandas as pd
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from data_processor import DataProcessor
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from visualizer import Visualizer
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from analyzer import Analyzer
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from openai_agent import OpenAIAgent
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st.set_page_config(page_title="AI-Powered Data Viz", layout="wide")
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st.title("AI-Powered Data Visualization")
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# File uploader
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uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
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if uploaded_file is not None:
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# Read the CSV file
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df = pd.read_csv(uploaded_file)
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# Initialize our components
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data_processor = DataProcessor(df)
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visualizer = Visualizer()
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analyzer = Analyzer()
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openai_agent = OpenAIAgent()
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# Data Cleaning and Preprocessing
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st.header("Data Cleaning and Preprocessing")
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st.write("Original data shape:", df.shape)
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st.write("Columns with missing values:", data_processor.get_columns_with_missing_values())
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if st.button("Clean Data"):
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df_cleaned = data_processor.clean_data()
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st.write("Cleaned data shape:", df_cleaned.shape)
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st.write(df_cleaned.head())
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# Data Visualization
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st.header("Data Visualization")
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columns = df.columns.tolist()
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x_axis = st.selectbox("Select X-axis", columns)
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y_axis = st.selectbox("Select Y-axis", columns)
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chart_type = st.radio("Select chart type", ["Scatter", "Line", "Bar"])
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if st.button("Generate Visualization"):
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fig = visualizer.create_plot(df, x_axis, y_axis, chart_type)
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st.plotly_chart(fig)
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# Data Analysis
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st.header("Data Analysis")
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analysis_prompt = st.text_input("Enter your analysis question:")
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if st.button("Analyze"):
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analysis_result = analyzer.analyze_data(df, analysis_prompt)
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st.write(analysis_result)
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# OpenAI Integration
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st.header("AI-Powered Insights")
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ai_prompt = st.text_area("Ask the AI for insights:")
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if st.button("Get AI Insights"):
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ai_response = openai_agent.get_insights(df, ai_prompt)
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st.write(ai_response)
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else:
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st.write("Please upload a CSV file to get started.")
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