Update app.py

app.py

@@ -2,7 +2,11 @@ import gradio as gr
 import base64
 from openai import OpenAI
 import glob
-import os
+import matplotlib.pyplot as plt
+import pandas as pd
+import gradio as gr
+import numpy as np
+
 png_files = glob.glob("*.png")
 
 YOUR_OPENROUTER_API_KEY = os.getenv('OPENROUTER_API_KEY')
@@ -40,7 +44,7 @@ vision_models = [
 
  
 text_models = ["meta-llama/llama-guard-4-12b",
-    "openai/gpt-oss-safeguard-20b"]
+    "meta-llama/llama-guard-2-8b"]
 
 phoenix_prompt = """
 You are PHOENIX, an advanced prompt-injection detective.
@@ -110,6 +114,59 @@ def test_injection(prompt, model):
         reply = f"Error with {model}: {e}"
     return f"=== {model} ===\n{reply}"
   
+
+def render_dashboard(df_input):
+    df = df_input.copy()
+    df['timestamp'] = pd.to_datetime(df['timestamp'])
+    df['scan_id'] = range(1, len(df) + 1)
+    df['risk_score'] = np.where(df['result'] == 'UNSAFE', 100, 0)
+    
+    unsafe_rate = df['risk_score'].mean()
+    top_model = df['model_used'].mode().iloc[0] if not df['model_used'].mode().empty else 'N/A'
+     
+    kpi_html = f"""
+    <div style="display: flex; gap: 20px; justify-content: center; flex-wrap: wrap;">
+        <div style="background: linear-gradient(135deg, #42a5f5, #2196f3); color: white; padding: 20px; border-radius: 12px; text-align: center; min-width: 150px; box-shadow: 0 4px 10px rgba(0,0,0,0.1);">
+            <h3>Risk Score</h3><h2>{unsafe_rate:.0f} / 100</h2>
+        </div>
+        <div style="background: linear-gradient(135deg, #ff9800, #f57c00); color: white; padding: 20px; border-radius: 12px; text-align: center; min-width: 150px; box-shadow: 0 4px 10px rgba(0,0,0,0.1);">
+            <h3>UNSAFE Rate</h3><h2>{unsafe_rate:.1f}%</h2>
+        </div>
+    </div>
+    """
+     
+    fig_line = plt.figure(figsize=(8, 4), facecolor='white')
+    plt.plot(df["scan_id"], df["risk_score"], color="black", marker="o", linewidth=2, markersize=6)
+ 
+    plt.title("Threat Detection Trend  ", fontsize=14, fontweight='bold', color='skyblue')
+    plt.xlabel("Scan Attempt #", color='skyblue')
+    plt.ylabel("Risk Score", color='skyblue')
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+    
+ 
+    result_counts = df["result"].value_counts()
+    fig_bar = plt.figure(figsize=(8, 4), facecolor='white')
+    plt.bar(result_counts.index, result_counts.values, color="black", alpha=0.7, edgecolor='white', linewidth=1.5)
+    plt.title("Detection Result Frequency  ", fontsize=14, fontweight='bold', color='skyblue')
+    plt.xlabel("Result Type", color='skyblue')
+    plt.ylabel("Count", color='skyblue')
+    plt.xticks(rotation=45)
+    plt.grid(True, alpha=0.3, axis='y')
+    plt.tight_layout()
+    
+    return (
+        kpi_html,
+        ", ".join(df['result'].unique()),
+        top_model,
+        "Enhance guardrails for top model",
+        df,
+        fig_line,
+        fig_bar
+    )
+
+
+
 light_blue_glass_css = """
 /* Background Gradient */
 body, .gradio-container {
@@ -195,7 +252,7 @@ theme = gr.themes.Glass(
     block_label_text_color="#1976d2",
     button_primary_text_color="#0d47a1"  )
 
- 
+
 with gr.Blocks(theme=theme, css=light_blue_glass_css) as demo:
     gr.Markdown(
         """
@@ -213,7 +270,6 @@ with gr.Blocks(theme=theme, css=light_blue_glass_css) as demo:
 
     with gr.Tabs():
         with gr.TabItem("  Image Scanner"):  
- 
             with gr.Row():
                 img = gr.Image(type="filepath", label="Target Source", value="sampleimg.png")
                 with gr.Column():
@@ -256,11 +312,28 @@ with gr.Blocks(theme=theme, css=light_blue_glass_css) as demo:
             )
             btn2.click(test_injection, inputs=[prompt, mdl_text], outputs=output)
 
-        with gr.TabItem("Prompt injection sources"):
-
+        with gr.TabItem("📊 Analytics Dashboard"):
+            gr.Markdown("# 🔍 Phoenikz Prompt Injection Analyzer - Analytics")
+            
+            df_loaded = gr.Dataframe(pd.read_csv('analytics.csv'), label="Data (Edit & Refresh)")
+            refresh_btn = gr.Button("🔄 Render Dashboard", variant="primary")
+            
+            kpi_display = gr.HTML(label="KPIs")
+            policy_list = gr.Textbox(label="Top Results", interactive=False)
+            model_used = gr.Textbox(label="Top Model", interactive=False)
+            mitigation = gr.Textbox(label="Recommendation", interactive=False)
+            data_table = gr.Dataframe(label="Full Log")
+            line_chart = gr.Plot(label="Threat Trend")
+            bar_chart = gr.Plot(label="Result Frequency")
+            
+            refresh_btn.click(render_dashboard, inputs=df_loaded, outputs=[kpi_display, policy_list, model_used, mitigation, data_table, line_chart, bar_chart])
+            
  
-          gr.Markdown(
-              """
+            demo.load(render_dashboard, inputs=df_loaded, outputs=[kpi_display, policy_list, model_used, mitigation, data_table, line_chart, bar_chart])
+
+        with gr.TabItem("Prompt injection sources"):
+            gr.Markdown(
+                """
             # 🛡️ AI Red Teaming & Safety – Learning Hub
 
             Below is a curated list of **10 high-signal sources** to track:
@@ -272,7 +345,6 @@ with gr.Blocks(theme=theme, css=light_blue_glass_css) as demo:
             Use these responsibly and ethically, in line with your organization’s security and compliance policies.
             """
           )
-          gr.Markdown(markdown_content)
-
+            gr.Markdown(markdown_content)
 
-demo.launch(share=True ,debug=True)
+demo.launch(share=True, debug=True)