Update app.py

app.py

@@ -67,20 +67,20 @@ def getModelPath(language):
     return(path)
 
 def inference(input_img,strategy,language):
-    if model_n == 'Image Captioning':
-        image = transform(raw_image).unsqueeze(0).to(device)   
-        with torch.no_grad():
-            if strategy == "Beam search":
-              cap = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5)
-            else:
-              cap = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5)
-            if modelpath:
-                translator = pipeline("translation", model=modelpath)
-                trans_cap = translator(cap[0])
-                tc = trans_cap[0]['translation_text']
-                return str(tc)
-            else:
-                return str(cap[0])
+    image = transform(input_img).unsqueeze(0).to(device)   
+    with torch.no_grad():
+        if strategy == "Beam search":
+            cap = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5)
+        else:
+            cap = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5)
+        modelpath = getModelPath(language)
+        if modelpath:
+            translator = pipeline("translation", model=modelpath)
+            trans_cap = translator(cap[0])
+            tc = trans_cap[0]['translation_text']
+            return str(tc)
+        else:
+            return str(cap[0])
 
     
 description = "A pipeline of BLIP image captioning and Helsinki translation in order to generate image captions in a language of your choice either with beam search (deterministic) or nucleus sampling (stochastic). Enjoy! Is the language you want to use missing? Let me know and I'll integrate it."