Update app.py

app.py

@@ -115,20 +115,53 @@ def cos_sim(a, b):
 
   
 #def Visual_re_ranker(caption, visual_context_label, visual_context_prob):
-def Visual_re_ranker(caption_man, caption_woman, visual_context_label, visual_context_prob):
+#def Visual_re_ranker(caption_man, caption_woman, visual_context_label, visual_context_prob):
+#    caption_man = caption_man  
+#    caption_woman = caption_woman
+#    visual_context_label= visual_context_label
+#    visual_context_prob = visual_context_prob
+#    caption_emb_man = model_sts.encode(caption_man, convert_to_tensor=True)
+#    caption_emb_woman = model_sts.encode(caption_woman, convert_to_tensor=True)
+#    visual_context_label_emb = model_sts.encode(visual_context_label, convert_to_tensor=True)
+
+#    sim_m =  cosine_scores = util.pytorch_cos_sim(caption_emb_man, visual_context_label_emb)
+#    sim_m = sim_m.cpu().numpy()
+#    sim_m = get_sim(sim_m)
+
+#    sim_w = cosine_scores = util.pytorch_cos_sim(caption_emb_woman, visual_context_label_emb) 
+#    sim_w = sim_w.cpu().numpy()
+#    sim_w = get_sim(sim_w)
+
+
+#    LM_man = cloze_prob(caption_man)
+#    LM_woman = cloze_prob(caption_woman)
+    #LM  = scorer.sentence_score(caption, reduce="mean")
+#    score_man     = pow(float(LM_man),pow((1-float(sim_m))/(1+ float(sim_m)),1-float(visual_context_prob)))
+#    score_woman   = pow(float(LM_woman),pow((1-float(sim_w))/(1+ float(sim_w)),1-float(visual_context_prob)))
+
+
+
+
+    #return {"LM": float(LM)/1, "sim": float(sim)/1, "score": float(score)/1 }
+#    return {"Man": float(score_man)/1, "Woman": float(score_woman)/1}
+    #return LM, sim, score 
+
+
+
+def Visual_re_ranker(caption_man, caption_woman, context_label, context_prob):
     caption_man = caption_man  
     caption_woman = caption_woman
-    visual_context_label= visual_context_label
-    visual_context_prob = visual_context_prob
+    context_label= context_label
+    context_prob = context_prob
     caption_emb_man = model_sts.encode(caption_man, convert_to_tensor=True)
     caption_emb_woman = model_sts.encode(caption_woman, convert_to_tensor=True)
-    visual_context_label_emb = model_sts.encode(visual_context_label, convert_to_tensor=True)
+    context_label_emb = model_sts.encode(context_label, convert_to_tensor=True)
 
-    sim_m =  cosine_scores = util.pytorch_cos_sim(caption_emb_man, visual_context_label_emb)
+    sim_m =  cosine_scores = util.pytorch_cos_sim(caption_emb_man, context_label_emb)
     sim_m = sim_m.cpu().numpy()
     sim_m = get_sim(sim_m)
 
-    sim_w = cosine_scores = util.pytorch_cos_sim(caption_emb_woman, visual_context_label_emb) 
+    sim_w = cosine_scores = util.pytorch_cos_sim(caption_emb_woman, context_label_emb) 
     sim_w = sim_w.cpu().numpy()
     sim_w = get_sim(sim_w)
 
@@ -136,8 +169,8 @@ def Visual_re_ranker(caption_man, caption_woman, visual_context_label, visual_co
     LM_man = cloze_prob(caption_man)
     LM_woman = cloze_prob(caption_woman)
     #LM  = scorer.sentence_score(caption, reduce="mean")
-    score_man     = pow(float(LM_man),pow((1-float(sim_m))/(1+ float(sim_m)),1-float(visual_context_prob)))
-    score_woman   = pow(float(LM_woman),pow((1-float(sim_w))/(1+ float(sim_w)),1-float(visual_context_prob)))
+    score_man     = pow(float(LM_man),pow((1-float(sim_m))/(1+ float(sim_m)),1-float(context_prob)))
+    score_woman   = pow(float(LM_woman),pow((1-float(sim_w))/(1+ float(sim_w)),1-float(context_prob)))
 
 
 
@@ -150,6 +183,7 @@ def Visual_re_ranker(caption_man, caption_woman, visual_context_label, visual_co
 
 
 
+
 demo = gr.Interface(
     fn=Visual_re_ranker,
     description="Demo for Women Wearing Lipstick: Measuring the Bias Between Object and Its Related Gender",