app

app.py

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+import spacy
+import wikipedia
+from wikipedia.exceptions import DisambiguationError
+from transformers import TFAutoModel, AutoTokenizer
+import numpy as np
+import pandas as pd
+
+try:
+  nlp = spacy.load("en_core_web_sm")
+except:
+  spacy.cli.download("en_core_web_sm")
+  nlp = spacy.load("en_core_web_sm")
+
+wh_words = ['what', 'who', 'how', 'when', 'which']
+def get_concepts(text):
+  text = text.lower()
+  doc = nlp(text)
+  concepts = []
+  for chunk in doc.noun_chunks:
+    if chunk.text not in wh_words:
+      concepts.append(chunk.text)
+  return concepts
+
+def get_passages(text, k=100):
+    doc = nlp(text)
+    passages = []
+    passage_len = 0
+    passage = ""
+    sents = list(doc.sents)
+    for i in range(len(sents)):
+        sen = sents[i]
+        passage_len+=len(sen)
+        if passage_len >= k:
+            passages.append(passage)
+            passage = sen.text
+            passage_len = len(sen)
+            continue
+
+        elif i==(len(sents)-1):
+            passage+=" "+sen.text
+            passages.append(passage)
+            passage = ""
+            passage_len = 0
+            continue
+
+        passage+=" "+sen.text
+    return passages
+
+def get_dicts_for_dpr(concepts, n_results=20, k=100):
+  dicts = []
+  for concept in concepts:
+    wikis = wikipedia.search(concept, results=n_results)
+    print(concept, "No of Wikis: ",len(wikis))
+    for wiki in wikis:
+        try:
+          html_page = wikipedia.page(title = wiki, auto_suggest = False)
+        except DisambiguationError:
+          continue
+
+        passages = get_passages(html_page.content, k=k)
+        for passage in passages:
+          i_dicts = {}
+          i_dicts['text'] = passage
+          i_dicts['title'] = wiki
+          dicts.append(i_dicts)
+  return dicts
+
+passage_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2")
+query_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2")
+
+p_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2")
+q_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2")
+
+def get_title_text_combined(passage_dicts):
+    res = []
+    for p in passage_dicts:
+        res.append(tuple((p['title'], p['text'])))
+    return res
+    
+def extracted_passage_embeddings(processed_passages, max_length=156):
+    passage_inputs = p_tokenizer.batch_encode_plus(
+                    processed_passages,
+                    add_special_tokens=True,
+                    truncation=True,
+                    padding="max_length",
+                    max_length=max_length,
+                    return_token_type_ids=True
+                )
+    passage_embeddings = passage_encoder.predict([np.array(passage_inputs['input_ids']), 
+                                                np.array(passage_inputs['attention_mask']), 
+                                                np.array(passage_inputs['token_type_ids'])], 
+                                                batch_size=64, 
+                                                verbose=1)
+    return passage_embeddings
+
+def extracted_query_embeddings(queries, max_length=64):
+    query_inputs = q_tokenizer.batch_encode_plus(
+                    queries,
+                    add_special_tokens=True,
+                    truncation=True,
+                    padding="max_length",
+                    max_length=max_length,
+                    return_token_type_ids=True
+                )
+    query_embeddings = query_encoder.predict([np.array(query_inputs['input_ids']), 
+                                                np.array(query_inputs['attention_mask']), 
+                                                np.array(query_inputs['token_type_ids'])], 
+                                                batch_size=1, 
+                                                verbose=1)
+    return query_embeddings
+    
+def search(question):
+  concepts = get_concepts(question)
+  print("concepts: ",concepts)
+  dicts = get_dicts_for_dpr(concepts, n_results=1)
+  print("dicts len: ", len(dicts))
+  processed_passages = get_title_text_combined(dicts)
+  passage_embeddings = extracted_passage_embeddings(processed_passages)
+  query_embeddings = extracted_query_embeddings([question])
+  faiss_index = faiss.IndexFlatL2(128)
+  faiss_index.add(passage_embeddings.pooler_output)
+  prob, index = faiss_index.search(query_embeddings.pooler_output, k=10)
+  return pd.DataFrame([dicts[i] for i in index[0]])
+
+import gradio as gr
+inp = gr.inputs.Textbox(lines=2, default=question, label="Question")
+out = gr.outputs.Dataframe(label="Answers")#gr.outputs.Textbox(label="Answers")
+gr.Interface(fn=search, inputs=inp, outputs=out).launch()