Update app.py

app.py

@@ -1,23 +1,26 @@
 import os
 import cv2
 import numpy as np
-import torch
 from ultralytics import YOLO
 import gradio as gr
 import traceback
+
 # -----------------------------
 # 1. YOLO model path
 # -----------------------------
-YOLO_MODEL_PATH = "best.pt"  # Push this small model to HF repo
+YOLO_MODEL_PATH = "best.pt"
+yolo_model = YOLO(YOLO_MODEL_PATH)
+yolo_model.eval()
 
 # -----------------------------
-# 2. Load YOLO model
+# 2. Reference alphabet image
 # -----------------------------
-yolo_model = YOLO(YOLO_MODEL_PATH)
-yolo_model.eval()
+REFERENCE_IMAGE_PATH = "asl_alphabet.webp"  # Web-friendly format
+reference_img = cv2.imread(REFERENCE_IMAGE_PATH)
+reference_img = cv2.cvtColor(reference_img, cv2.COLOR_BGR2RGB)
 
 # -----------------------------
-# 3. Inference function
+# 3. Prediction function
 # -----------------------------
 def predict_asl(image):
     try:
@@ -25,16 +28,14 @@ def predict_asl(image):
             raise ValueError("No image uploaded")
         
         img = image.copy()
-        h, w, _ = img.shape
-        print(f"🔹 Uploaded image shape: {img.shape}, dtype: {img.dtype}")
 
-        # --- YOLO prediction directly on NumPy array ---
-        results = yolo_model.predict(img, imgsz=300, verbose=False)[0]
+        # YOLO prediction
+        results = yolo_model.predict(img, imgsz=320, verbose=False)[0]
         pred_idx = results.probs.top1
         pred_label = results.names[pred_idx]
         confidence = results.probs.data[pred_idx].item()
 
-        # Overlay prediction text on original image
+        # Overlay prediction text
         cv2.putText(
             img,
             f"{pred_label} ({confidence:.2f})",
@@ -46,7 +47,10 @@ def predict_asl(image):
             cv2.LINE_AA
         )
 
-        return cv2.cvtColor(img, cv2.COLOR_BGR2RGB), pred_label, round(confidence, 2)
+        # Combine uploaded image and reference alphabet side by side
+        combined_img = np.hstack([cv2.cvtColor(img, cv2.COLOR_BGR2RGB), reference_img])
+
+        return combined_img, pred_label, round(confidence, 2)
 
     except Exception as e:
         print("❌ Error in predict_asl:", e)
@@ -57,13 +61,13 @@ def predict_asl(image):
 # 4. Gradio Interface
 # -----------------------------
 title = "🖐️ ASL Letter Classifier"
-description = "Upload a hand sign image and see the predicted letter and confidence."
+description = "Upload a hand sign image and compare it with the full ASL alphabet."
 
 iface = gr.Interface(
     fn=predict_asl,
-    inputs=gr.Image(type="numpy"),
+    inputs=gr.Image(type="numpy", label="Upload your ASL Letter"),
     outputs=[
-        gr.Image(type="numpy", label="Original Image with Prediction"),
+        gr.Image(type="numpy", label="Your Image with Prediction & Alphabet Reference"),
         gr.Textbox(label="Predicted Letter"),
         gr.Textbox(label="Confidence")
     ],
@@ -73,4 +77,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch(ssr_mode=False, share = True)
+    iface.launch(share=True)