Refactor app.py and requirements.txt; remove SemanticSearcher and update numpy version

app.py

@@ -5,7 +5,6 @@ import pandas as pd
 from datasets import load_dataset
 
 from src.genai import GenAI
-from src.semantic_searcher import SemanticSearcher
 from src.upvote_predictor import UpvotePredictor
 
 # Load the dataset
@@ -52,8 +51,8 @@ examples = "\n".join(
 
 # Initialize the SemanticSearcher
 genai = GenAI()
-upvote_predictor = UpvotePredictor("models/bert_model")
-_ = SemanticSearcher(df_counsel_chat_topic)
+upvote_predictor = UpvotePredictor("src/bert_model")
+# _ = SemanticSearcher(df_counsel_chat_topic)
 
 
 def get_output(question: str, question_context: str = None) -> str:

requirements.txt

@@ -46,7 +46,7 @@ mpmath==1.3.0
 multidict==6.1.0; python_version >= '3.8'
 multiprocess==0.70.16; python_version >= '3.8'
 networkx==3.4.2; python_version >= '3.10'
-numpy==2.2.3; python_version >= '3.10'
+numpy==1.26.4; python_version >= '3.9'
 openai==1.66.3; python_version >= '3.8'
 orjson==3.10.15; python_version >= '3.8'
 packaging==24.2; python_version >= '3.8'

src/bert_model

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cc67a668274575af4cda22c97d41acbb6fb11a23a8871a8f7bce9a579ba6ea51
-size 438062089

src/semantic_searcher.py

@@ -11,8 +11,7 @@ class SemanticSearcher:
             show_progress_bar=True,
             convert_to_tensor=True,
         )
-
-    def retrieve_relevant_qna(self, question: str, question_context: str = None) -> str:
+    def retrieve_relevant_qna(self, question: str, question_context: str = None):
         if question_context is None:
             question_context = ""
         query = question + "\n" + question_context

src/upvote_predictor.py

@@ -4,8 +4,6 @@ import torch
 from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
 from transformers import BertTokenizer
 
-from src.semantic_searcher import retrieve_relevant_qna
-
 
 class UpvotePredictor:
     def __init__(self, model_path: str):
@@ -65,7 +63,7 @@ class UpvotePredictor:
         if predictions[0] == 0:
             return (
                 "Not credible suggestion",
-                retrieve_relevant_qna(question, question_context),
+                pd.DataFrame(),
             )
         else:
             return ("Credible suggestion", pd.DataFrame())