Update app.py

app.py

@@ -440,21 +440,21 @@ def compute_tfidf_embeddings(documents1, documents2):
 
 
 def extract_eval(image1,image2,image3,image4):
-    # ideal_text=extract(image1)
-    # print("Extracting Ideal Text \n")
-    # print(ideal_text)
-    # submitted_text=extract(image3)
-    # print("Extracting Submitted Text \n")
-    # print(submitted_text_text)
-    # a,b=sim(ideal_text,submitted_text)
-    # print(a)
-    # text_sim_score=b
-    # model = ResNet50(include_top=False, weights='imagenet', pooling='avg')
-    diagram_1_embed=return_image_embedding(model,image2)
-    diagram_2_embed=return_image_embedding(model,image4)
-    diagram_embed_sim_score=util.pytorch_cos_sim(embedding_1, embedding_2)
-    print("Diagram Embedding Similarity Score \n")
-    print(diagram_embed_sim_score)
+    ideal_text=extract(image1)
+    print("Extracting Ideal Text \n")
+    print(ideal_text)
+    submitted_text=extract(image3)
+    print("Extracting Submitted Text \n")
+    print(submitted_text_text)
+    a,b=sim(ideal_text,submitted_text)
+    print(a)
+    text_sim_score=b
+    model = ResNet50(include_top=False, weights='imagenet', pooling='avg')
+    # diagram_1_embed=return_image_embedding(model,image2)
+    # diagram_2_embed=return_image_embedding(model,image4)
+    # diagram_embed_sim_score=util.pytorch_cos_sim(embedding_1, embedding_2)
+    # print("Diagram Embedding Similarity Score \n")
+    # print(diagram_embed_sim_score)