Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import smolagent
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def preprocess_data(file):
+    try:
+        df = pd.read_csv(file.name) if file.name.endswith('.csv') else pd.read_excel(file.name)
+        agent = smolagent.SmolAgent()
+        cleaned_df = agent.run("Clean and preprocess this dataset", df)
+        return cleaned_df.describe().to_string()
+    except Exception as e:
+        return f"Error in preprocessing: {str(e)}"
+
+def generate_insights(file):
+    try:
+        df = pd.read_csv(file.name) if file.name.endswith('.csv') else pd.read_excel(file.name)
+        agent = smolagent.SmolAgent()
+        insights = agent.run("Generate insights and report on this dataset", df)
+        return insights
+    except Exception as e:
+        return f"Error in generating insights: {str(e)}"
+
+def visualize_data(file):
+    try:
+        df = pd.read_csv(file.name) if file.name.endswith('.csv') else pd.read_excel(file.name)
+        agent = smolagent.SmolAgent()
+        important_features = agent.run("Identify important features in this dataset", df)
+        
+        if not isinstance(important_features, dict):
+            return "Error: Expected a dictionary of feature importance values."
+        
+        fig, ax = plt.subplots(figsize=(8, 6))
+        sns.barplot(x=list(important_features.keys()), y=list(important_features.values()), ax=ax)
+        ax.set_title("Feature Importance")
+        plt.xticks(rotation=45)
+        return fig
+    except Exception as e:
+        return f"Error in visualization: {str(e)}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("## AI-Powered Data Analysis with SmolAgent")
+    file_input = gr.File(label="Upload CSV or Excel File")
+    
+    preprocess_btn = gr.Button("Preprocess Data")
+    preprocess_output = gr.Textbox()
+    preprocess_btn.click(preprocess_data, inputs=file_input, outputs=preprocess_output)
+    
+    insights_btn = gr.Button("Generate Insights")
+    insights_output = gr.Textbox()
+    insights_btn.click(generate_insights, inputs=file_input, outputs=insights_output)
+    
+    visualize_btn = gr.Button("Visualize Data")
+    visualize_output = gr.Plot()
+    visualize_btn.click(visualize_data, inputs=file_input, outputs=visualize_output)
+    
+demo.launch()