it works locally!!

app.py

@@ -4,36 +4,36 @@ from minicons import cwe
 import pandas as pd
 import numpy as np
 
-from model import FeatureNormPredictor
+from model import FFNModule, FeatureNormPredictor, FFNParams, TrainingParams
 
 
 def predict (Word, Sentence, LM, Layer, Norm):
+    models = {'bert': 'bert-base-uncased'}
     if Word not in Sentence: return "invalid input: word not in sentence"
     model_name = LM + str(Layer) + '_to_' + Norm
-    lm = cwe.CWE('bert-base-uncased')
+    lm = cwe.CWE(models[LM])
     if Layer not in range (lm.layers): return "invalid input: layer not in lm"
 
-    labels = "These are some fake features".split(" ")
-    vals = np.random.randint(-10,10,(5))
-    return model_name+" \n"+"\n".join([labels[i]+" "+str(vals[i]) for i in range(len(labels)) if vals[i]>0])
-    # model = FeatureNormPredictor.load_from_checkpoint(
-    #     checkpoint_path=model_name+'.ckpt',
-    #     map_location=None
-    #     )
-    # model.eval()
-
-    # with open (model_name+'.txt', "r") as file:
-    #     labels = [line.rstrip() for line in file.readlines()]
-
-    # data = (Word, Sentence)
-    # embs = lm.extract_representation(data, layer=8)
-    # avg = embs.sum(0)/len(data)
-    # pred = torch.nn.functional.relu(model(avg))
-    # pred = pred.squeeze(0)
-    # pred_list = pred.detach().numpy().tolist()
+    # labels = "These are some fake features".split(" ")
+    # vals = np.random.randint(-10,10,(5))
+    # return model_name+" \n"+"\n".join([labels[i]+" "+str(vals[i]) for i in range(len(labels)) if vals[i]>0])
+    model = FeatureNormPredictor.load_from_checkpoint(
+        checkpoint_path='models/'+model_name+'.ckpt',
+        map_location=None
+    )
+    model.eval()
+
+    with open ('models/'+model_name+'.txt', "r") as file:
+        labels = [line.rstrip() for line in file.readlines()]
+
+    data = (Sentence, Word)
+    emb = lm.extract_representation(data, layer=8)
+    pred = torch.nn.functional.relu(model(emb))
+    pred = pred.squeeze(0)
+    pred_list = pred.detach().numpy().tolist()
     
-    # output = [labels[i]+'\t'+str(pred_list[i]) for i in range(len(labels)) if pred_list[i] > 0.0]
-    # return "All Positive Predicted Values:\n"+"\n".join(output)
+    output = [labels[i]+'\t'+str(pred_list[i]) for i in range(len(labels)) if pred_list[i] > 0.0]
+    return "All Positive Predicted Values:\n"+"\n".join(output)
 
 demo = gr.Interface(
     fn=predict,
@@ -47,4 +47,7 @@ demo = gr.Interface(
     outputs=["text"],
 )
 
-demo.launch()
+demo.launch()
+
+if __name__ == "__main__":
+    demo.launch()