updaed

app.py

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+# app.py
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+import io
+
+df = pd.DataFrame()
+
+def load_csv(file):
+    global df
+    try:
+        df = pd.read_csv(file.name)
+
+        required_cols = {"Name", "Age", "Salary", "Performance Score"}
+        if not required_cols.issubset(df.columns):
+            return None, f"❌ CSV must contain {required_cols}"
+
+        # Add Age Group
+        df["Age Group"] = pd.cut(df["Age"],
+                                 bins=[20, 25, 30, 35, 40],
+                                 labels=["21-25", "26-30", "31-35", "36-40"])
+        return df, "✅ File loaded successfully!"
+    except Exception as e:
+        return None, f"❌ Error: {e}"
+
+
+def show_analysis():
+    if df.empty:
+        return "⚠️ Please load a CSV first!"
+    avg_salary = df["Salary"].mean()
+    top_salary = df.loc[df["Salary"].idxmax(), "Name"]
+    top_perf = df.loc[df["Performance Score"].idxmax(), "Name"]
+
+    return f"""
+    📊 **Average Salary:** {avg_salary:.2f}  
+    💰 **Highest Salary Holder:** {top_salary}  
+    🏆 **Top Performer:** {top_perf}
+    """
+
+
+def salary_chart():
+    if df.empty:
+        return None
+    plt.figure(figsize=(8, 5))
+    plt.bar(df["Name"], df["Salary"], color="skyblue", edgecolor="black")
+    plt.title("Employee Salaries", fontsize=14)
+    plt.ylabel("Salary")
+    plt.xticks(rotation=45)
+    plt.grid(axis="y", linestyle="--", alpha=0.7)
+    plt.tight_layout()
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+    return buf
+
+
+def performance_chart():
+    if df.empty:
+        return None
+    plt.figure(figsize=(8, 5))
+    plt.plot(df["Name"], df["Performance Score"], marker="o",
+             color="green", linewidth=2, markersize=8)
+    plt.title("Employee Performance Scores", fontsize=14)
+    plt.ylabel("Performance Score")
+    plt.xticks(rotation=45)
+    plt.grid(True, linestyle="--", alpha=0.6)
+    plt.tight_layout()
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+    return buf
+
+
+def search_employee(query):
+    if df.empty:
+        return None
+    if not query.strip():
+        return df
+    return df[df["Name"].str.lower().str.contains(query.lower())]
+
+
+# Build Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## ✨ Employee Data Analysis Dashboard ✨")
+
+    with gr.Row():
+        file_input = gr.File(label="📂 Upload CSV", file_types=[".csv"])
+        status = gr.Textbox(label="Status", interactive=False)
+
+    with gr.Row():
+        search_box = gr.Textbox(label="🔍 Search Employee")
+        analysis_btn = gr.Button("📊 Show Analysis")
+    
+    data_table = gr.Dataframe(headers=["Name", "Age", "Salary", "Performance Score", "Age Group"], label="Employee Data")
+    analysis_output = gr.Markdown()
+    
+    with gr.Row():
+        salary_btn = gr.Button("💰 Salary Chart")
+        performance_btn = gr.Button("🏆 Performance Chart")
+
+    salary_plot = gr.Image(type="auto", label="Salary Chart")
+    performance_plot = gr.Image(type="auto", label="Performance Chart")
+
+    # Events
+    file_input.change(load_csv, inputs=file_input, outputs=[data_table, status])
+    search_box.submit(search_employee, inputs=search_box, outputs=data_table)
+    analysis_btn.click(show_analysis, inputs=None, outputs=analysis_output)
+    salary_btn.click(salary_chart, inputs=None, outputs=salary_plot)
+    performance_btn.click(performance_chart, inputs=None, outputs=performance_plot)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+pandas
+matplotlib