Update app.py

app.py

@@ -2,6 +2,7 @@ import os
 import tempfile
 from pathlib import Path
 from typing import List
+import numpy as np
 
 import gradio as gr
 import pandas as pd
@@ -158,24 +159,53 @@ def document_summary(df: pd.DataFrame) -> pd.DataFrame:
 
 
 # =========================
-# VISUALIZATION
+# GAUGE VISUALIZATION
 # =========================
-def generate_confidence_plot(df: pd.DataFrame) -> str:
-    probs = df[df["Text Chunk"] != "📄 Document Summary"]["AI Probability (%)"]
+def generate_gauge(prob_percent: float, prediction: str) -> str:
+    fig, ax = plt.subplots(figsize=(6, 3))
+
+    angles = np.linspace(np.pi, 0, 100)
+
+    # Background arc
+    ax.plot(np.cos(angles), np.sin(angles), linewidth=20, alpha=0.15)
+
+    # Colored arc
+    for i, val in enumerate(np.linspace(0, 100, 99)):
+        if val < 40:
+            color = "green"
+        elif val < 70:
+            color = "orange"
+        else:
+            color = "red"
+
+        ax.plot(
+            np.cos(angles[i:i + 2]),
+            np.sin(angles[i:i + 2]),
+            linewidth=20,
+            color=color
+        )
+
+    # Needle
+    needle_angle = np.pi * (1 - prob_percent / 100)
+    ax.plot(
+        [0, 0.8 * np.cos(needle_angle)],
+        [0, 0.8 * np.sin(needle_angle)],
+        linewidth=4
+    )
 
-    fig, ax = plt.subplots()
-    ax.hist(probs, bins=10)
-    ax.axvline(AI_THRESHOLD * 100, linestyle="--")
-    ax.set_title("AI Probability Distribution")
-    ax.set_xlabel("AI Probability (%)")
-    ax.set_ylabel("Number of Chunks")
+    # Text
+    ax.text(0, -0.1, f"{prob_percent:.0f}%", ha="center", va="center", fontsize=24, weight="bold")
+    ax.text(0, -0.32, prediction, ha="center", va="center", fontsize=12)
+
+    ax.set_aspect("equal")
+    ax.axis("off")
 
     with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
-        fig.savefig(tmp.name, bbox_inches="tight")
-        plot_path = tmp.name
+        fig.savefig(tmp.name, bbox_inches="tight", dpi=150)
+        path = tmp.name
 
     plt.close(fig)
-    return plot_path
+    return path
 
 
 # =========================
@@ -203,9 +233,14 @@ def run_detector(text_input: str, uploaded_files, progress=gr.Progress()):
 
     df = classify_chunks(chunks, progress)
     final_df = document_summary(df)
-    plot_path = generate_confidence_plot(final_df)
 
-    return final_df, plot_path
+    summary_row = final_df[final_df["Text Chunk"] == "📄 Document Summary"].iloc[0]
+    gauge_path = generate_gauge(
+        summary_row["AI Probability (%)"],
+        summary_row["Prediction"]
+    )
+
+    return final_df, gauge_path
 
 
 # =========================
@@ -214,8 +249,8 @@ def run_detector(text_input: str, uploaded_files, progress=gr.Progress()):
 with gr.Blocks(title="🧪 Offline AI Document Detector") as app:
     gr.Markdown("## 🧪 Offline AI Document Detector")
     gr.Markdown(
-        "Detect whether text is AI-generated using an **offline, open-source model**. "
-        "Supports **PDF, DOCX, TXT, and pasted text**. Optimized for CPU-only environments."
+        "Detect whether content is AI-generated using an **offline, open-source model**. "
+        "Supports **PDF, DOCX, TXT, and pasted text**. Optimized for **CPU-only Hugging Face Spaces**."
     )
 
     text_input = gr.Textbox(
@@ -231,12 +266,12 @@ with gr.Blocks(title="🧪 Offline AI Document Detector") as app:
 
     analyze_btn = gr.Button("🔍 Analyze")
     output_table = gr.Dataframe(label="📊 Detection Results")
-    confidence_plot = gr.Image(label="📈 AI Probability Distribution")
+    gauge_plot = gr.Image(label="🧠 AI Probability Gauge")
 
     analyze_btn.click(
         fn=run_detector,
         inputs=[text_input, file_input],
-        outputs=[output_table, confidence_plot]
+        outputs=[output_table, gauge_plot]
     )
 
 if __name__ == "__main__":