Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+from sklearn.impute import SimpleImputer
+from io import BytesIO
+import warnings
+
+warnings.filterwarnings("ignore")
+
+# Function to read and process uploaded file
+def read_file(file):
+    if file.name.endswith(".csv"):
+        df = pd.read_csv(file)
+    elif file.name.endswith(".xlsx"):
+        df = pd.read_excel(file)
+    else:
+        raise ValueError("Unsupported file format. Please upload a CSV or Excel file.")
+    return df
+
+# Clean the data
+def clean_data(df):
+    # Drop duplicates
+    df = df.drop_duplicates()
+    # Fill missing values
+    imputer = SimpleImputer(strategy="most_frequent")
+    df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
+    return df
+
+# Generate summary statistics
+def generate_summary(df):
+    return df.describe(include="all").transpose()
+
+# Correlation heatmap
+def generate_correlation_heatmap(df):
+    numeric_df = df.select_dtypes(include=[np.number])
+    corr = numeric_df.corr()
+    plt.figure(figsize=(10, 8))
+    sns.heatmap(corr, annot=True, cmap="coolwarm", fmt=".2f")
+    buf = BytesIO()
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+    plt.close()
+    return buf
+
+# Feature importance using Random Forest
+def feature_importance(df):
+    # Encode categorical variables
+    df_encoded = df.copy()
+    label_encoders = {}
+    for col in df_encoded.select_dtypes(include="object").columns:
+        le = LabelEncoder()
+        df_encoded[col] = le.fit_transform(df_encoded[col])
+        label_encoders[col] = le
+
+    # Target variable selection
+    target_column = df_encoded.columns[-1]
+    X = df_encoded.iloc[:, :-1]
+    y = df_encoded[target_column]
+
+    # Fit Random Forest
+    model = RandomForestClassifier(random_state=42)
+    model.fit(X, y)
+
+    # Get feature importance
+    importance = pd.DataFrame({
+        "Feature": X.columns,
+        "Importance": model.feature_importances_
+    }).sort_values(by="Importance", ascending=False)
+
+    return importance
+
+# Visualize feature importance
+def plot_feature_importance(importance):
+    plt.figure(figsize=(10, 6))
+    sns.barplot(x="Importance", y="Feature", data=importance)
+    plt.title("Feature Importance")
+    buf = BytesIO()
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+    plt.close()
+    return buf
+
+# Main analysis function
+def analyze_file(file):
+    try:
+        # Step 1: Read file
+        df = read_file(file)
+
+        # Step 2: Clean data
+        df_cleaned = clean_data(df)
+
+        # Step 3: Generate summary statistics
+        summary = generate_summary(df_cleaned)
+
+        # Step 4: Generate correlation heatmap
+        heatmap_buf = generate_correlation_heatmap(df_cleaned)
+
+        # Step 5: Feature importance analysis
+        importance = feature_importance(df_cleaned)
+        importance_plot_buf = plot_feature_importance(importance)
+
+        # Step 6: Return results
+        return (
+            summary,
+            heatmap_buf,
+            importance.head(10),  # Top 10 important features
+            importance_plot_buf,
+        )
+    except Exception as e:
+        return str(e)
+
+# Gradio Interface
+def gradio_interface():
+    with gr.Blocks() as interface:
+        gr.Markdown("# AI Data Analytics Tool")
+        gr.Markdown("Upload your dataset in CSV or Excel format to analyze and generate insights automatically.")
+
+        with gr.Row():
+            file_input = gr.File(label="Upload your CSV or Excel file")
+            analyze_button = gr.Button("Analyze")
+
+        with gr.Row():
+            summary_output = gr.Dataframe(label="Summary Statistics")
+            heatmap_output = gr.Image(label="Correlation Heatmap")
+            importance_output = gr.Dataframe(label="Feature Importance")
+            importance_plot_output = gr.Image(label="Feature Importance Plot")
+
+        analyze_button.click(
+            analyze_file,
+            inputs=file_input,
+            outputs=[summary_output, heatmap_output, importance_output, importance_plot_output],
+        )
+
+    return interface
+
+# Launch the Gradio interface
+interface = gradio_interface()
+interface.launch(debug=True)