dummy output again until figure out model upload

app.py

@@ -13,31 +13,34 @@ def predict (Word, Sentence, LM, Layer, Norm):
     lm = cwe.CWE('bert-base-uncased')
     if Layer not in range (lm.layers): return "invalid input: layer not in lm"
 
-    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))
+    print(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()
     
-    output = [labels[i]+'\t'+str(pred_list[i]) for i in range(len(labels)) if 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,
     inputs=[
         "text", 
         "text", 
-        gr.Radio(["bert", "roberta", "electra"]),
+        gr.Radio(["bert", "albert"]),
         "number",
         gr.Radio(["binder", "mcrae", "buchanan"]),
     ],