Update app.py

app.py

@@ -427,11 +427,11 @@ def draw_boxes(image, bounds, color='yellow', width=2):
 
 def inference(img, lang):
     reader = easyocr.Reader(lang)
-    bounds = reader.readtext(img)
+    bounds = reader.readtext(img,detail=0)
     #im = PIL.Image.open(img.name)
     #draw_boxes(im, bounds)
     #im.save('result.jpg')
-    return [pd.DataFrame(bounds).iloc[: , 1:]]
+    return bounds
 
 def compute_tfidf_embeddings(documents1, documents2):
     # Combine both lists of words into a single list
@@ -470,8 +470,12 @@ def extract_eval(Ideal_Answer_Text,Ideal_Answer_Diagram,Submitted_Answer_Text,Su
     print(str(int(float(str(diagram_embed_sim_score).split("[")[2].split("]")[0])*10.0)))
     diagram_1_text=inference(Ideal_Answer_Diagram,['en'])
     diagram_2_text=inference(Submitted_Answer_Diagram,['en'])
-    print(pd.DataFrame(diagram_1_text['text']).to_numpy())
-    print(pd.DataFrame(diagram_1_text['text']).to_numpy())
+    print(pd.DataFrame(diagram_1_text)
+    print(pd.DataFrame(diagram_2_text)
+    diagram_1_text_embed,diagram_2_text_embed=compute_tfidf_embeddings(diagram_1_text,diagram_2_text)
+    diagram_text_similarity=util.pytorch_cos_sim(diagram_1_text_embed,diagram_2_text_embed)
+    print("Diagram Text Embedding Similarity Score \n")
+    print(str(int(float(str(diagram_text_similarity).split("[")[2].split("]")[0])*10.0)))