Update app.py

app.py

@@ -44,15 +44,6 @@ val_transform = transforms.Compose([
                          [0.229, 0.224, 0.225])
 ])
 
-# ----------------------------
-# Font helper
-# ----------------------------
-def _get_font(size):
-    try:
-        return ImageFont.truetype("DejaVuSans-Bold.ttf", size=size)
-    except Exception:
-        return ImageFont.load_default()
-
 # ----------------------------
 # Predict
 # ----------------------------
@@ -71,23 +62,17 @@ def predict_image(img_path: str):
     conf = float(top_prob.item()) * 100.0
     predicted_breed = breeds[int(top_idx.item())]
 
-    # Annotate using PIL
-    annotated = img.copy()
-    draw = ImageDraw.Draw(annotated)
-    font = _get_font(max(20, int(annotated.width * 0.05)))
+    # Draw text directly on image
+    draw = ImageDraw.Draw(img)
+    font = ImageFont.load_default()
     text = f"{predicted_breed} ({conf:.2f}%)"
+    draw.rectangle([0, 0, img.width, 30], fill="black")
+    draw.text((10, 5), text, fill="white", font=font)
 
-    # background rectangle
-    text_w, text_h = draw.textsize(text, font=font)
-    draw.rectangle([0, 0, text_w + 20, text_h + 20], fill="black")
-    draw.text((10, 10), text, fill="white", font=font)
-
-    # save annotated image
-    safe_breed = "".join(c if c.isalnum() or c in ('_', '-') else "_" for c in predicted_breed)
-    annotated_name = f"{safe_breed}_{conf:.2f}pct_{stem}.png"
-    annotated.save(annotated_name)
+    annotated_name = f"{predicted_breed}_{conf:.2f}pct_{stem}.png"
+    img.save(annotated_name)
 
-    # save CSV
+    # CSV output
     df = pd.DataFrame([{
         "breed": predicted_breed,
         "confidence_percent": f"{conf:.2f}%",
@@ -96,32 +81,24 @@ def predict_image(img_path: str):
     csv_name = f"{stem}_prediction.csv"
     df.to_csv(csv_name, index=False)
 
-    return predicted_breed, round(conf, 2), annotated_name, annotated_name, csv_name
+    return predicted_breed, f"{conf:.2f}%", annotated_name, csv_name
+
 
 # ----------------------------
 # UI
 # ----------------------------
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("<h1 style='text-align:center;'>🐄 GoVed AI — Indian Cattle/Buffalo Breed Detection</h1>")
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            img_input = gr.Image(type="filepath", label="📤 Upload Image", height=340)
-            detect_btn = gr.Button("🔍 Detect Breed", variant="primary")
-
-        with gr.Column(scale=1):
-            breed_output = gr.Textbox(label="Predicted Breed", interactive=False)
-            confidence_output = gr.Number(label="Confidence (%)", interactive=False, precision=2)
-            annotated_preview = gr.Image(type="filepath", label="Annotated Preview", height=340)
-            with gr.Row():
-                img_download = gr.File(label="⬇️ Download Image", type="filepath")
-                csv_download = gr.File(label="⬇️ Download CSV", type="filepath")
-
-    detect_btn.click(
-        fn=predict_image,
-        inputs=img_input,
-        outputs=[breed_output, confidence_output, annotated_preview, img_download, csv_download],
-    )
+demo = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="filepath", label="📤 Upload Cattle/Buffalo Image"),
+    outputs=[
+        gr.Textbox(label="Predicted Breed"),
+        gr.Textbox(label="Confidence (%)"),
+        gr.File(label="⬇️ Download Annotated Image", type="filepath"),
+        gr.File(label="⬇️ Download Prediction CSV", type="filepath"),
+    ],
+    title="🐄 GoVed AI – Indian Cattle/Buffalo Breed Detection",
+    description="Upload an image → Get predicted breed, confidence, annotated image, and CSV download."
+)
 
 if __name__ == "__main__":
     demo.launch()