Update app.py

app.py

@@ -1,3 +1,118 @@
+
 import gradio as gr
+import os
+import numpy as np
+os.system("pip install pdfminer.six rank_bm25 torch transformers")
+
+from gradio.mix import Series
+#import re
+from rank_bm25 import BM25Okapi
+import string 
+import torch
+from transformers import pipeline
+import pdfminer
+from pdfminer.high_level import extract_text
+
+len_doc = 500
+overlap = 15 
+param_top_k_retriver = 15
+param_top_k_ranker = 3
+
+def read_pdf(file):
+  text = extract_text(file.name)
+  # Split text into smaller docs
+  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):
+    stop_w = ['a', 'the', 'am', 'is' , 'are', 'who', 'how', 'where', 'when', 'why', 'what']
+    tokenized_doc = []
+    for token in text.lower().split():
+        token = token.strip(string.punctuation)
+
+        if len(token) > 0 and token not in stop_w:
+            tokenized_doc.append(token)
+    return tokenized_doc
+
+def retrieval(query, top_k_retriver, docs, bm25_):
+
+    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
+
+def qa_ranker(query, docs_, top_k_ranker, qa_model):
+    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 cstr(s, color='black'):
+    return "<text style=color:{}>{}</text>".format(color, s)
+def cstr_bold(s, color='black'):
+    return "<text style=color:{}><b>{}</b></text>".format(color, s)
+def cstr_break(s, color='black'):
+    return "<text style=color:{}><br>{}</text>".format(color, s)
+
+def print_colored(text, start_idx, end_idx, confidence):
+    conf_str = '- Confidence: ' +  confidence
+    a = cstr(' '.join([text[:start_idx], \
+                        cstr_bold(text[start_idx:end_idx], color='blue'), \
+                        text[end_idx:], \
+                        cstr_break(conf_str, color='grey')]), color='black')
+    return a
+       
+def final_qa_pipeline(file, query, model_nm):
+    docs = read_pdf(file)
+    tokenized_corpus = []
+    for doc in docs:
+        tokenized_corpus.append(bm25_tokenizer(doc))
+    
+    bm25 = BM25Okapi(tokenized_corpus)
+    
+    top_k_retriver, top_k_ranker = param_top_k_retriver, param_top_k_ranker
+    lvl1 = retrieval(query, top_k_retriver, docs, bm25)
+
+    qa_model = pipeline("question-answering", 
+                    #model = "deepset/minilm-uncased-squad2")
+                    model = "deepset/"+ str(model_nm))
+
+    if len(lvl1) > 0:
+        fnl_rank = qa_ranker(query, [l["docs"] for l in lvl1], top_k_ranker,qa_model)
+        top1 = print_colored(fnl_rank[0]['doc'], fnl_rank[0]['start'], fnl_rank[0]['end'], str(np.round(100*fnl_rank[0]["score"],1))+"%")  
+        if len(lvl1)>1:
+            top2 = print_colored(fnl_rank[1]['doc'], fnl_rank[1]['start'], fnl_rank[1]['end'], str(np.round(100*fnl_rank[1]["score"],1))+"%")
+        else:
+            top2 = "None"
+        return (top1, top2)
+    else:
+        return ("No match","No match")
+        
+examples = [
+    ]
 
+iface = gr.Interface(
+   fn = final_qa_pipeline,
+   inputs = [gr.inputs.File(label="input pdf file"), gr.inputs.Textbox(label="Question:"), gr.inputs.Dropdown(choices=["minilm-uncased-squad2","roberta-base-squad2"],label="Model")],
+   outputs = [gr.outputs.HTML(label="Top 1 answer"), gr.outputs.HTML(label="Top 2 answer")],
+   examples=examples,
+   theme = "grass",
+   title = "Question Answering on annual reports",
+   description = "Navigate long annual reports by using Machine learning to answer your questions. \nSimply upload any annual report pdf you are interested in and ask model a question OR load an example from below."
+   )
+iface.launch(enable_queue = True)
 gr.Interface.load("models/deepset/roberta-base-squad2").launch()