Update app.py

app.py

@@ -1,44 +1,26 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-tokenizer = AutoTokenizer.from_pretrained("Evelyn18/roberta-base-spanish-squades-becasv3")
-model = AutoModelForCausalLM.from_pretrained("Evelyn18/roberta-base-spanish-squades-becasv3")
-
-def predict(input, history=[]):
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
-
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+import os
+import sys
+from transformers import pipeline
+import gradio as gr
 
-    # generate a response 
-    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+model = pipeline('question-answering', model='deepset/tinyroberta-squad2', tokenizer='deepset/tinyroberta-squad2')
 
-    # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    response.remove("")
-    
-    # write some HTML
-    html = "<div class='chatbot'>"
-    for m, msg in enumerate(response):
-        cls = "user" if m%2 == 0 else "bot"
-        html += "<div class='msg {}'> {}</div>".format(cls, msg)
-    html += "</div>"
-    
-    return html, history
+def qa(passage, question):
+    question = question
+    context = passage
+    nlp_input = {
+        'question': question,
+        'context': context
+    }
 
-import gradio as gr
+    return model(nlp_input)['answer']
 
-css = """
-.chatbox {display:flex;flex-direction:column}
-.msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
-.msg.user {background-color:cornflowerblue;color:white}
-.msg.bot {background-color:lightgray;align-self:self-end}
-.footer {display:none !important}
-"""
+passage = "The quick brown fox jumped over the lazy dog."
+question = "Who jumps over the lazy dog?"
 
-gr.Interface(fn=predict,
-             theme="default",
-             inputs=[gr.inputs.Textbox(placeholder="How are you?"), "state"],
-             outputs=["html", "state"],
-             css=css).launch()
+iface = gr.Interface(qa,
+                        title="Question Answering using RoBERTa",
+                        inputs=[gr.inputs.Textbox(lines=15), "text"],
+                        outputs=["text"],
+                        examples=[["{}".format(passage), "{}".format(question)]])
+iface.launch()