model.py

model.py

@@ -8,7 +8,7 @@ from transformers import pipeline
 from fusion import compute_fusion
 import torch
 import time
-
+import os
 torch.set_grad_enabled(False)
 
 device = 0 if torch.cuda.is_available() else -1
@@ -85,6 +85,13 @@ Confidence: **{text_conf}%**
     if image is not None:
         try:
             results = image_pipeline(image)
+            # create image folder if not exists
+            os.makedirs("saved_images", exist_ok=True)
+
+            image_path = None
+            if image is not None:
+                image_path = f"saved_images/{int(time.time())}.png"
+                image.save(image_path)
 
             image_result_display = "## 🖼 Image Classification\n\n"
 
@@ -170,6 +177,7 @@ Confidence: **{audio_conf}%**
         "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
         "text": text,
         "image": image_label,
+        "image_path": image_path,
         "audio": audio_label,
         "transcription": transcription,
         "fusion_score": round(fusion_score,2)