Create app.py

app.py

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+import gradio as gr
+from gradio.mix import Series
+import re
+from rank_bm25 import BM25Okapi
+import string 
+from transformers import pipeline
+import pdfminer
+from pdfminer.high_level import extract_text
+from termcolor import colored
+
+def read_pdf(file):
+  text = extract_text(file)
+  # Split text into smaller docs
+  len_doc = 400
+  overlap = 50 
+  docs = []
+  
+  i = 0
+  while i < len(text):
+      docs.append(text[i:i+len_doc])
+      i = i + len_doc - overlap
+  return docs
+  
+  # We use BM25 as retriver which will do 1st round of candidate filtering based on word based matching
+
+def bm25_tokenizer(text):
+    tokenized_doc = []
+    for token in text.lower().split():
+        token = token.strip(string.punctuation)
+
+        if len(token) > 0 and token not in _stop_words.ENGLISH_STOP_WORDS:
+            tokenized_doc.append(token)
+    return tokenized_doc
+
+tokenized_corpus = []
+for doc in docs:
+    tokenized_corpus.append(bm25_tokenizer(doc))
+
+bm25 = BM25Okapi(tokenized_corpus)
+
+def retrieval(query, top_k_retriver, docs):
+
+    bm25_scores = bm25.get_scores(bm25_tokenizer(query))
+    top_n = np.argsort(bm25_scores)[::-1][:top_k_retriver]
+    bm25_hits = [{'corpus_id': idx, 
+                  'score': bm25_scores[idx], 
+                  'docs':docs[idx]} for idx in top_n if bm25_scores[idx] > 0]
+    bm25_hits = sorted(bm25_hits, key=lambda x: x['score'], reverse=True)
+    
+    return bm25_hits
+
+qa_model = pipeline("question-answering", 
+                    model = "huggingface/deepset/roberta-base-squad2")
+                    
+def qa_ranker(query, docs_, top_k_ranker):
+    ans = []
+    for doc in docs_:
+        answer = qa_model(question = query, 
+                            context = doc)
+        answer['doc'] = doc
+        ans.append(answer)
+    return sorted(ans, key=lambda x: x['score'], reverse=True)[:top_k_ranker]
+    
+def final_qa_pipeline(file, query):
+    docs = read_pdf(file)
+    top_k_retriver, top_k_ranker = 10,1
+    lvl1 = retrieval(query, top_k_retriver, docs)
+
+    if len(lvl1) > 0:
+        fnl_rank = qa_ranker(query, [l["docs"] for l in lvl1], top_k_ranker)
+        return (fnl_rank[0]["answer"], fnl_rank[0]["score"])
+        #for fnl_ in fnl_rank:
+        #    print("\n")
+        #    print_colored(fnl_['doc'], fnl_['start'], fnl_['end'])
+        #    print(colored("Confidence score of ") + colored(str(fnl_['score'])[:4], attrs=['bold']))
+    else:
+        return ("No match", 0)
+        
+iface = gr.Interface(
+   fn = pdf_to_text,
+   inputs = '[gr.inputs.File(label="input pdf file"), gr.inputs.Textbox(label="Question:")],
+   outputs = [gr.outputs.HTML(label="Answer"), gr.outputs.HTML(label="Score")]
+   )
+iface.launch()