Delete demo.py

demo.py

@@ -1,56 +0,0 @@
-#!/usr/bin/env python3
-from doctest import OutputChecker
-import sys
-import argparse
-import torch
-import re
-import os
-import gradio as gr
-from sentence_transformers import SentenceTransformer, util
-from sklearn.metrics.pairwise import cosine_similarity
-from lm_scorer.models.auto import AutoLMScorer as LMScorer
-from sentence_transformers import SentenceTransformer, util
-from sklearn.metrics.pairwise import cosine_similarity
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-model = SentenceTransformer('stsb-distilbert-base', device=device)
-batch_size = 1
-scorer = LMScorer.from_pretrained('gpt2' , device=device, batch_size=batch_size)
-
-
-def cos_sim(a, b):
-    return np.inner(a, b) / (np.linalg.norm(a) * (np.linalg.norm(b)))
-
-
-  
-def Visual_re_ranker(caption, visual_context_label, visual_context_prob):
-    caption = caption 
-    visual_context_label= visual_context_label
-    visual_context_prob = visual_context_prob
-    caption_emb = model.encode(caption, convert_to_tensor=True)
-    visual_context_label_emb = model.encode(visual_context_label, convert_to_tensor=True)
-
-
-    sim =  cosine_scores = util.pytorch_cos_sim(caption_emb, visual_context_label_emb)
-    sim = sim.cpu().numpy()
-    sim = str(sim)[1:-1]
-    sim = str(sim)[1:-1] 
-
-    LM  = scorer.sentence_score(caption, reduce="mean")
-    score = pow(float(LM),pow((1-float(sim))/(1+ float(sim)),1-float(visual_context_prob)))
-    
-
-    #return {"LM": float(LM)/1, "sim": float(sim)/1, "score": float(score)/1 }
-    return {"init hypothesis": float(LM)/1, "Visual Belief Revision": float(score)/1 }
-    #return LM, sim, score 
-
-
-
-demo = gr.Interface(
-    fn=Visual_re_ranker,
-    description="Demo for Belief Revision based Caption Re-ranker with Visual Semantic Information",
-    inputs=[gr.Textbox(value="a city street filled with traffic at night") , gr.Textbox(value="traffic"),  gr.Textbox(value="0.7458009")],
-    #outputs=[gr.Textbox(value="Language Model Score") , gr.Textbox(value="Semantic Similarity Score"),  gr.Textbox(value="Belief revision score via visual context")],
-    outputs="label",
-)
-demo.launch()