Update app.py

app.py

@@ -451,27 +451,27 @@ def compute_tfidf_embeddings(documents1, documents2):
 
 import requests
 import base64
-def extract_eval(image1,image2,image3,image4):
+def extract_eval(Ideal_Answer_Text,Ideal_Answer_Diagram,Submitted_Answer_Text,Submitted_Answer_Diagram):
     # print(image1)
-    # ideal_text=extract(image1)
+    # ideal_text=extract(Ideal_Answer_Text)
     # print("Extracting Ideal Text \n")
     # print(ideal_text)
-    # submitted_text=extract(image3)
+    # submitted_text=extract(Submitted_Answer_Text)
     # print("Extracting Submitted Text \n")
     # print(submitted_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_1_embed=return_image_embedding(model,Ideal_Answer_Diagram)
+    diagram_2_embed=return_image_embedding(model,Submitted_Answer_Diagram)
     diagram_embed_sim_score=util.pytorch_cos_sim(diagram_1_embed, diagram_2_embed)
     print("Diagram Embedding Similarity Score \n")
     print(str(int(float(str(diagram_embed_sim_score).split("[")[2].split("]")[0])*10.0)))
-    diagram_1_text=inference(image2,['en'])
-    diagram_2_text=inference(image4,['en'])
-    print(diagram_1_text)
-    print(diagram_2_text)
+    diagram_1_text=inference(Ideal_Answer_Diagram,['en'])
+    diagram_2_text=inference(Submitted_Answer_Diagram,['en'])
+    print(pd.DataFrame(diagram_1_text[0])to_numpy())
+    print(pd.DataFrame(diagram_1_text[0])to_numpy())