Update app.py

app.py

@@ -1,19 +1,11 @@
 import gradio as gr
+import torch
 import torch.nn as nn
-class Model(nn.Module):
-  def __init__(self, model_name='bert_model'):
-    super(Model, self).__init__()
-    self.bert = transformers.BertModel.from_pretrained(config['MODEL_ID'], return_dict=False)
-    self.bert_drop = nn.Dropout(0.0)
-    self.out = nn.Linear(config['HIDDEN_SIZE'], config['NUM_LABELS'])
-    self.model_name = model_name
-    def forward(self, ids, mask, token_type_ids):
-      _, o2 = self.bert(ids, attention_mask = mask, token_type_ids = token_type_ids)
-      bo = self.bert_drop(o2)
-      output = self.out(bo)
-      return output
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("juanpasanper/tigo_question_answer")
+model = AutoModelForCausalLM.from_pretrained("juanpasanper/tigo_question_answer")
    
-model = Model(model_name=este_si_me_sirvio.bin)
 model.load_state_dict(torch.load(juanpasanper/tigo_question_answer))
 def question_answer(context, question):
   predictions, raw_outputs = model.predict([{"context": context, "qas": [{"question": question, "id": "0",}],}])