fix(remove_tf): Unblock container build by removing tf dependency

requirements.txt

@@ -11,6 +11,4 @@ streamlit
 transformers[torch]
 langchain_openai
 langchain_google_genai
-tf-keras
-tensorflow
 torch

src/graph/state_vector_nodes.py

@@ -7,13 +7,14 @@ from state.state import StateVector
 from streamlitui.constants import *
 import torch
 import torch.nn.functional as F
-import tensorflow as tf
+#import tensorflow as tf
 import re
 from langchain_openai import ChatOpenAI
 from langchain_community.tools.semanticscholar.tool import SemanticScholarQueryRun
 from langchain_community.utilities.semanticscholar import SemanticScholarAPIWrapper
 from langchain_community.tools.tavily_search import TavilySearchResults
 import pandas as pd
+import torch.nn.functional as F
 
 
 class question_model:
@@ -48,12 +49,16 @@ class question_model:
             text=state.get('seed_question').lower(),
             truncation=True,
             padding=True,
-            return_tensors="tf")
-        output = self.distilbert_model(predict_input)[0]
-        #prediction_value = torch.argmax(output, dim=1).cpu().numpy()
+            return_tensors="pt")
+        output = self.distilbert_model(predict_input.numpy())[0]
+        numpy_output=output.numpy()
+        torch_output=torch.from_numpy(numpy_output)
+        prediction_value = torch.argmax(torch_output, dim=1).numpy()  # All answers
+        prob_value=F.softmax(torch_output).numpy()[0]
+
         #prob_value = F.softmax(output, dim=1).cpu().numpy()[0]
-        prediction_value = tf.argmax(output, axis=1).numpy()#All answers
-        prob_value=tf.nn.softmax(output).numpy()[0]#Probability of TF output
+        #prediction_value = tf.argmax(output, axis=1).numpy()#All answers
+        #prob_value=tf.nn.softmax(output).numpy()[0]#Probability of TF output
         Topic_Bool=prob_value>0.4
         Topics=[]
         Keywords={}