Update app.py

app.py

@@ -63,6 +63,13 @@ import pandas as pd
 import random
 import os
 import csv
+import numpy as np
+import pickle
+from rank_bm25 import BM25Okapi
+from nltk.tokenize import word_tokenize
+loaded_texts = np.load('texts.npy', allow_pickle=True)
+with open('bm25_model.pkl', 'rb') as file:
+  bm25 = pickle.load(file)
 
 # france credentials
 BASE_URL = "https://cnerg-gpt-france.openai.azure.com/"
@@ -178,10 +185,42 @@ def function3(input_text,one_shot_example,n):
 
 
 def function4(input_text, one_shot_example, n):
-  n=int(n)
+    tokenized_query = word_tokenize(input_text.lower())
+    doc_scores = bm25.get_scores(tokenized_query)
+    sorted_docs = [doc for _, doc in sorted(zip(doc_scores, loaded_texts), reverse=True)]
+    n=int(n)
+    k=""
+    for doc in sorted_docs[:n]:
+        k+=doc
+    model = AzureChatOpenAI(
+      openai_api_base=BASE_URL,
+      openai_api_version="2023-05-15",
+      deployment_name=DEPLOYMENT_NAME,
+      openai_api_key=API_KEY,
+      openai_api_type="azure",
+    )
+    if len(one_shot_example)==0:
+        combined_input = f"please provide comprehensive and well-researched responses to the following question. Ensure that the information is up-to-date and includes relevant scientific insights and data , context:{k} \n\n question : {input_text}"
+        generated_answer = model(
+              [
+                  HumanMessage(
+                      content=combined_input
+                  )
+              ]
+          )
+        return generated_answer.content
+    else:
+        
+        combined_input = f"please provide comprehensive and well-researched responses to the following question. Ensure that the information is up-to-date and includes relevant scientific insights and data  \n\n context:{k} \n\n,Below is an example question-answer pair for reference\n\n {one_shot_example} \n\n  Now answer this question \n\n question :{input_text}"
+        generated_answer = model(
+              [
+                  HumanMessage(
+                      content=combined_input
+                  )
+              ]
+          )
+        return generated_answer.content
 
-    # Your logic for function 4
-  return f"Output of Function 4 with input: {input_text}, one shot example: {one_shot_example} and parameter: {parameter}"
 
 # Define the dropdown options
 dropdown_options = ["1", "2", "3"]