Update pages/type_text.py

pages/type_text.py

@@ -139,22 +139,6 @@ selected_st_model = st.selectbox('Current selected Sentence Transformer model:',
 ## Get the selected SentTrans model
 SentTrans_model = st_models[selected_st_model]
 
-## Define the Reasoning models
-rs_models = {
-    '(medium speed) original model for general domain: meta-llama/Llama-3.2-1B-Instruct': 'meta-llama/Llama-3.2-1B-Instruct',
-    '(slower speed) original model for general domain: Qwen/Qwen2-1.5B-Instruct': 'Qwen/Qwen2-1.5B-Instruct',
-    '(medium speed) original model for general domain: EpistemeAI/ReasoningCore-1B-r1-0': 'EpistemeAI/ReasoningCore-1B-r1-0',
-    '(expected in future) fine-tuned model for medical domain: meta-llama/Llama-3.2-1B-Instruct': 'meta-llama/Llama-3.2-1B-Instruct',
-    '(expected in future) fine-tuned model for medical domain: Qwen/Qwen2-1.5B-Instruct': 'Qwen/Qwen2-1.5B-Instruct',
-}
-
-## Create the select Reasoning box
-selected_rs_model = st.selectbox('Current selected Reasoning model:', list(rs_models.keys())) # or 'Choose a Reasoning Model'
-#st.write("Current selection:", selected_rs_model)
-
-## Get the selected Reasoning model
-Reasoning_model = rs_models[selected_rs_model]
-
 ## Load the Sentence Transformer model ...
 @st.cache_resource
 def load_model():
@@ -163,13 +147,7 @@ def load_model():
 
 model = load_model()
 
-## Load the Reasoning model as pipeline ...
-@st.cache_resource
-def load_pipe():
-    pipe = pipeline("text-generation", model=Reasoning_model, device_map=device,) # device_map="auto", torch_dtype=torch.bfloat16
-    return pipe
 
-pipe = load_pipe()
 
 # Semantic search, Compute cosine similarity between INTdesc_embedding and SBS descriptions
 INTdesc_embedding = model.encode(INTdesc_input)
@@ -197,6 +175,32 @@ if INTdesc_input and st.button(":blue[Map to SBS codes]", key="run_st_model"): #
 
     st.dataframe(data=dfALL, hide_index=True)
 
+
+    ## Define the Reasoning models
+    rs_models = {
+        '(medium speed) original model for general domain: meta-llama/Llama-3.2-1B-Instruct': 'meta-llama/Llama-3.2-1B-Instruct',
+        '(slower speed) original model for general domain: Qwen/Qwen2-1.5B-Instruct': 'Qwen/Qwen2-1.5B-Instruct',
+        '(medium speed) original model for general domain: EpistemeAI/ReasoningCore-1B-r1-0': 'EpistemeAI/ReasoningCore-1B-r1-0',
+        '(expected in future) fine-tuned model for medical domain: meta-llama/Llama-3.2-1B-Instruct': 'meta-llama/Llama-3.2-1B-Instruct',
+        '(expected in future) fine-tuned model for medical domain: Qwen/Qwen2-1.5B-Instruct': 'Qwen/Qwen2-1.5B-Instruct',
+    }
+
+    ## Create the select Reasoning box
+    selected_rs_model = st.selectbox('Current selected Reasoning model:', list(rs_models.keys())) # or 'Choose a Reasoning Model'
+    #st.write("Current selection:", selected_rs_model)
+
+    ## Get the selected Reasoning model
+    Reasoning_model = rs_models[selected_rs_model]
+
+    ## Load the Reasoning model as pipeline ...
+    @st.cache_resource
+    def load_pipe():
+        pipe = pipeline("text-generation", model=Reasoning_model, device_map=device,) # device_map="auto", torch_dtype=torch.bfloat16
+        return pipe
+
+    pipe = load_pipe()
+
+
     display_format = "ask REASONING MODEL: Which, if any, of the following SBS descriptions corresponds best to " + INTdesc_input +"? "
     #st.write(display_format)
     question = "Which one, if any, of the following Saudi Billing System descriptions A, B, C, D, or E corresponds best to " + INTdesc_input +"? "