Update app.py

app.py

@@ -1,11 +1,10 @@
 import gradio as gr
-from PIL import Image, ImageStat
-from fpdf import FPDF
+from PIL import Image, ImageStat, ImageDraw, ImageFont
 import io
 
-# -----------------------------
+# ===========================
 # EURISTIC AI DETECTOR
-# -----------------------------
+# ===========================
 def heuristic_ai_detector(image):
     if image.size[0] < 10 or image.size[1] < 10:
         return "Realistic", 0
@@ -24,84 +23,74 @@ def heuristic_ai_detector(image):
     label = "AI Generated" if score >= 50 else "Realistic"
     return label, score
 
-# -----------------------------
-# HEATMAP
-# -----------------------------
+# ===========================
+# GENERA HEATMAP
+# ===========================
 def generate_heatmap(image, score):
     base = image.convert("RGBA")
     overlay = Image.new("RGBA", base.size, (255, 0, 0, int(score*2.55)))
     return Image.alpha_composite(base, overlay)
 
-# -----------------------------
-# PDF in-memory (funziona HF Spaces)
-# -----------------------------
-def create_pdf(image, text):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-    pdf.multi_cell(0, 6, txt=text)
-
-    # Convert image in RGB e salva in BytesIO
-    buf_img = io.BytesIO()
-    image.convert("RGB").save(buf_img, format="PNG")
-    buf_img.seek(0)
-
-    # Salva immagine nel PDF usando fpdf
-    temp_img_path = "/tmp/temp_image.png"
-    with open(temp_img_path, "wb") as f:
-        f.write(buf_img.read())
-    pdf.image(temp_img_path, x=10, y=pdf.get_y()+10, w=180)
-
-    # Salva PDF in BytesIO per Gradio
-    pdf_bytes = io.BytesIO()
-    pdf.output(pdf_bytes)
-    pdf_bytes.seek(0)
-    pdf_bytes.name = "report.pdf"  # GRADIO richiede .name per scaricare
-    return pdf_bytes
-
-# -----------------------------
+# ===========================
 # FUNZIONE PRINCIPALE
-# -----------------------------
+# ===========================
 def check_image(image):
     label, score = heuristic_ai_detector(image)
+    heatmap = generate_heatmap(image, score)
+
+    # Colori output box testuale
+    if score < 40:
+        color = "#4CAF50"  # verde
+    elif score < 70:
+        color = "#FFC107"  # giallo
+    else:
+        color = "#F44336"  # rosso
+
+    # Testo formattato HTML
     if label == "AI Generated":
-        explanation = (
-            "⚠️ Questa immagine potrebbe essere generata da AI.\n"
-            "- Rumore basso / texture uniforme\n"
-            "- Dimensioni insolite\n"
-            "- Metadata EXIF assente\n"
-        )
-        reliability = "Bassa"
+        explanation = f"""
+        <div style="border-radius:10px; padding:15px; background-color:{color}; color:white;">
+        ⚠️ <b>AI Generated Image</b><br>
+        Score AI stimato: {score:.1f}%<br>
+        Rumore basso / texture uniforme<br>
+        Dimensioni insolite<br>
+        Metadata EXIF assente
+        </div>
+        """
     else:
-        explanation = (
-            "✅ Questa immagine sembra reale.\n"
-            "- Rumore naturale\n"
-            "- Dimensioni coerenti\n"
-            "- Metadata EXIF presente\n"
-        )
-        reliability = "Alta"
+        explanation = f"""
+        <div style="border-radius:10px; padding:15px; background-color:{color}; color:white;">
+        ✅ <b>Realistic Image</b><br>
+        Score AI stimato: {score:.1f}%<br>
+        Rumore naturale<br>
+        Dimensioni coerenti<br>
+        Metadata EXIF presente
+        </div>
+        """
 
-    output_text = f"{explanation}Score AI stimato: {score:.1f}%\nLivello affidabilità: {reliability}"
-    heatmap = generate_heatmap(image, score)
-    pdf = create_pdf(image, output_text)
-    return heatmap, output_text, pdf
+    return heatmap, explanation
+
+# ===========================
+# GRADIO BLOCKS MODERNO
+# ===========================
+with gr.Blocks(css="""
+body { background-color: #0d1117; font-family: 'Montserrat', sans-serif; color: white; }
+h1 { text-align: center; font-size: 40px; margin-bottom: 0px; color: #FFD700; }
+h3 { text-align: center; font-weight: normal; color: #CCCCCC; }
+.gr-button { background: linear-gradient(90deg, #FFD700, #FFAA00); color:black; font-weight:bold; border-radius:10px; height:50px; }
+.gr-box { background-color: #161B22; border-radius: 15px; padding: 20px; }
+""") as demo:
+
+    gr.HTML("<h1>💎 Image Trust Checker 2026</h1><h3>Verifica se le immagini sono generate da AI o realistiche</h3>")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            img_input = gr.Image(label="Carica immagine", type="pil")
+            analyze_btn = gr.Button("Analizza immagine")
+        with gr.Column(scale=1):
+            img_output = gr.Image(label="Heatmap sospetta", type="pil")
+            txt_output = gr.HTML(label="Risultato")
 
-# -----------------------------
-# INTERFACCIA GRADIO
-# -----------------------------
-iface = gr.Interface(
-    fn=check_image,
-    inputs=gr.Image(type="pil", label="Carica immagine"),
-    outputs=[
-        gr.Image(type="pil", label="Evidenziazione sospetta"),
-        gr.Textbox(label="Analisi dettagliata"),
-        gr.File(label="Scarica report PDF")
-    ],
-    title="💎 Image Trust Checker 2026 (Stable Version)",
-    description=(
-        "Analizza un'immagine per determinare quanto è affidabile.\n"
-        "Basato su euristiche open-source, heatmap e PDF compatibili con HF Spaces."
-    ),
-)
+    analyze_btn.click(fn=check_image, inputs=img_input, outputs=[img_output, txt_output])
 
-iface.launch()
+demo.launch()