Update app.py

app.py

@@ -1,25 +1,19 @@
 import gradio as gr
 import pandas as pd
-from retrieval import *
+from load_data import *
 import os
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
 
-model_name = "deepset/deberta-v3-large-squad2"
 
-# a) Get predictions
-nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
 
 
 
 hf_writer = gr.HuggingFaceDatasetSaver('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', "Pavankalyan/chitti_data")
 
-df = pd.read_csv("Responses.csv")
-text = list(df["text"].values)
-
 
 def chitti(query):
-    re_table = search(query, text)
+    re_table = search(query)
     answers_re_table = [re_table[i][0] for i in range(0,5)]
+    answer_links = [re_table[i][3] for i in range(0,5)]
     sorted_indices = sorted(range(len(answers_re_table)), key=lambda k: len(answers_re_table[k]))
     repeated_answers_indices =list()
     for i in range(4):
@@ -27,19 +21,15 @@ def chitti(query):
             repeated_answers_indices.append(sorted_indices[i])
     for idx in repeated_answers_indices:
         answers_re_table.pop(idx)
+        answer_links.pop(idx)
         
-    QA_input = {'question': query,'context': answers_re_table[0]}
-    res1 = nlp(QA_input)['answer']
-    QA_input = {'question': query,'context': answers_re_table[1]}
-    res2 = nlp(QA_input)['answer']
     #return [res1,answers_re_table[0],res2,answers_re_table[1]]
-    return [answers_re_table[0],answers_re_table[1]]
+    return [answers_re_table[0],answers_links[0],answers_re_table[1],answer_links[1]]
 
 demo = gr.Interface(
     fn=chitti,
     inputs=["text"],
-    #outputs=["text","text","text","text"],
-    outputs=["text","text"],
+    outputs=["text","text","text","text"],
     allow_flagging = "manual",
     flagging_options = ["0","1","None"],
     flagging_callback=hf_writer