Create app.py

app.py

@@ -0,0 +1,78 @@
+import wikipedia as wiki
+import pprint as pp
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline, AutoModelForSeq2SeqLM
+import torch
+import gradio as gr
+
+def greet(name):
+    #question = 'Why is the sky blue?'
+    question = name
+    
+    results = wiki.search(question)
+    #print("Wikipedia search results for our question:\n")
+    #pp.pprint(results)
+    
+    page = wiki.page(results[0])
+    text = page.content
+    #print(f"\nThe {results[0]} Wikipedia article contains {len(text)} characters.")
+    
+    #print(text)
+    
+    
+    model_name = "deepset/roberta-base-squad2"
+    
+    #from transformers import AutoModel
+    
+    #model_name = AutoModelForQuestionAnswering.from_pretrained('./roberta-base-squad2/')
+    
+    def get_sentence(text, pos):
+        start = text.rfind('.', 0, pos) + 1
+        end = text.find('.', pos)
+        if end == -1:
+            end = len(text)
+        return text[start:end].strip()
+    
+    
+    # a) Get predictions
+    nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+    QA_input = {
+        'question': question,
+        'context': text
+    }
+    res = nlp(QA_input)
+    
+    # b) Load model & tokenizer
+    model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    print("{'answer': '"+res['answer']+"', 'text': '")
+    #print(res['answer'])
+    #print("', 'text': '")
+    
+    position = res['start']
+    #words = sum(map(str.split, text), [])
+    #sentence = ' '.join(words[position-1:]).split('.')[0] + '.'
+    
+    print(get_sentence(text, position)+'.')
+    
+    tokenizer = AutoTokenizer.from_pretrained("tuner007/pegasus_paraphrase")
+    
+    model = AutoModelForSeq2SeqLM.from_pretrained("tuner007/pegasus_paraphrase")
+    
+    torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    
+    def get_response(input_text,num_return_sequences,num_beams):
+      batch = tokenizer([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
+      translated = model.generate(**batch,max_length=60,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5)
+      tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
+      return tgt_text
+    
+    
+    num_beams = 20
+    num_return_sequences = 1
+    context = get_sentence(text, position)+'.'
+    print(get_response(context,num_return_sequences,num_beams)[0])
+    print("'}")
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+demo.launch()