Update app.py

app.py

@@ -1,18 +1,103 @@
 import streamlit as st
+import pandas as pd
+import seaborn as sns
+import plotly.express as px
 import os
 
-# Predefined code snippets for common components
-PREDEFINED_COMPONENTS = {
-    "Title": "st.title(app_title)",
-    "Subtitle": "st.subheader(app_subtitle)",
-    "Side Panel": "st.sidebar.title(side_panel_title)",
-    # Add more predefined components as needed
+# Predefined analysis tasks and visualization types
+PREDEFINED_ANALYSIS = {
+    "Basic Statistics": {
+        "Description": "Generate basic statistics summary for the dataset.",
+        "Code": "st.write(df.describe())"
+    },
+    "Correlation Heatmap": {
+        "Description": "Generate a correlation heatmap for numeric columns.",
+        "Code": "st.write(df.corr())\nst.write(sns.heatmap(df.corr(), annot=True, cmap='coolwarm'))"
+    },
+    "Histogram": {
+        "Description": "Generate a histogram for a selected numeric column.",
+        "Code": """
+selected_column = st.selectbox("Select a numeric column for the histogram", df.select_dtypes(include='number').columns)
+st.write(px.histogram(df, x=selected_column))
+"""
+    },
+    "Box Plot": {
+        "Description": "Generate a box plot for a selected numeric column.",
+        "Code": """
+selected_column = st.selectbox("Select a numeric column for the box plot", df.select_dtypes(include='number').columns)
+st.write(px.box(df, y=selected_column))
+"""
+    },
+    "Scatter Plot": {
+        "Description": "Generate a scatter plot for two selected numeric columns.",
+        "Code": """
+x_column = st.selectbox("Select X-axis column", df.select_dtypes(include='number').columns)
+y_column = st.selectbox("Select Y-axis column", df.select_dtypes(include='number').columns)
+st.write(px.scatter(df, x=x_column, y=y_column))
+"""
+    },
+    "Line Plot": {
+        "Description": "Generate a line plot for a selected numeric column.",
+        "Code": """
+selected_column = st.selectbox("Select a numeric column for the line plot", df.select_dtypes(include='number').columns)
+st.write(px.line(df, x=df.index, y=selected_column))
+"""
+    },
+    "Bar Chart": {
+        "Description": "Generate a bar chart for a selected categorical column.",
+        "Code": """
+selected_column = st.selectbox("Select a categorical column for the bar chart", df.select_dtypes(include='object').columns)
+st.write(px.bar(df, x=selected_column))
+"""
+    },
+    "Pair Plot": {
+        "Description": "Generate a pair plot for pairwise relationships between numeric columns.",
+        "Code": "st.write(sns.pairplot(df))"
+    },
+    "Distribution Plot": {
+        "Description": "Generate a distribution plot for a selected numeric column.",
+        "Code": """
+selected_column = st.selectbox("Select a numeric column for the distribution plot", df.select_dtypes(include='number').columns)
+st.write(sns.displot(df[selected_column], kde=True))
+"""
+    },
+    "Count Plot": {
+        "Description": "Generate a count plot for a selected categorical column.",
+        "Code": """
+selected_column = st.selectbox("Select a categorical column for the count plot", df.select_dtypes(include='object').columns)
+st.write(sns.countplot(data=df, x=selected_column))
+"""
+    },
+    "Pie Chart": {
+        "Description": "Generate a pie chart for a selected categorical column.",
+        "Code": """
+selected_column = st.selectbox("Select a categorical column for the pie chart", df.select_dtypes(include='object').columns)
+st.write(px.pie(df, names=selected_column))
+"""
+    },
+    "Area Plot": {
+        "Description": "Generate an area plot for a selected numeric column.",
+        "Code": """
+selected_column = st.selectbox("Select a numeric column for the area plot", df.select_dtypes(include='number').columns)
+st.write(px.area(df, x=df.index, y=selected_column))
+"""
+    },
+    "Violin Plot": {
+        "Description": "Generate a violin plot for a selected numeric column.",
+        "Code": """
+selected_column = st.selectbox("Select a numeric column for the violin plot", df.select_dtypes(include='number').columns)
+st.write(px.violin(df, y=selected_column))
+"""
+    },
 }
 
-def generate_streamlit_app_code(app_title, app_subtitle, side_panel_title, app_content, requirements):
+def generate_streamlit_app_code(app_title, app_subtitle, side_panel_title, analysis_tasks, requirements):
     # Generate Python code for the Streamlit app
     code = f"""
 import streamlit as st
+import pandas as pd
+import seaborn as sns
+import plotly.express as px
 
 {requirements}
 
@@ -20,7 +105,14 @@ def main():
     st.title("{app_title}")
     st.subheader("{app_subtitle}")
     st.sidebar.title("{side_panel_title}")
-    {app_content}
+
+    @st.cache
+    def load_data():
+        return pd.read_csv("your_data.csv")  # Replace "your_data.csv" with the path to your dataset
+
+    df = load_data()
+
+    {analysis_tasks}
 
 if __name__ == "__main__":
     main()
@@ -35,28 +127,23 @@ def main():
     app_subtitle = st.text_input("Enter your Streamlit app subtitle:")
     side_panel_title = st.text_input("Enter your Streamlit app side panel title:")
 
-    # Input for Streamlit app content
-    app_content = st.text_area("Enter your Streamlit app content (Python code):")
-
-    # Include predefined components
-    selected_components = st.multiselect("Select predefined components to include:", list(PREDEFINED_COMPONENTS.keys()))
+    # Select predefined analysis tasks
+    selected_tasks = st.multiselect("Select predefined analysis tasks to include:", list(PREDEFINED_ANALYSIS.keys()))
 
     # Generate requirements.txt file
     requirements = st.text_area("Enter requirements.txt content (optional):")
 
-    # File uploader for CSV or Excel files
-    uploaded_file = st.file_uploader("Upload a CSV or Excel file", type=["csv", "xlsx"])
-
     if st.button("Generate and Download"):
         if app_title:
-            # Include selected predefined components in the app content
-            for component in selected_components:
-                app_content += f"\n{PREDEFINED_COMPONENTS[component]}"
+            # Include selected predefined analysis tasks in the app content
+            analysis_tasks_code = ""
+            for task in selected_tasks:
+                analysis_tasks_code += f"\n# {PREDEFINED_ANALYSIS[task]['Description']}\n{PREDEFINED_ANALYSIS[task]['Code']}\n"
 
             # Write generated code to a .py file
             file_path = f"{app_title.replace(' ', '_').lower()}_app.py"
             with open(file_path, "w") as f:
-                f.write(generate_streamlit_app_code(app_title, app_subtitle, side_panel_title, app_content, requirements))
+                f.write(generate_streamlit_app_code(app_title, app_subtitle, side_panel_title, analysis_tasks_code, requirements))
 
             # Write requirements.txt file
             if requirements: