Update pages/type_text.py

pages/type_text.py

@@ -147,6 +147,32 @@ def load_model():
 
 model = load_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('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()
+
+
 # Semantic search, Compute cosine similarity between INTdesc_embedding and SBS descriptions
 INTdesc_embedding = model.encode(INTdesc_input)
 SBScorpus_embeddings = model.encode(SBScorpus)
@@ -175,32 +201,6 @@ if INTdesc_input and st.button(":blue[Map to SBS codes]", key="run_st_model"): #
     #st.markdown('<div id="bottom"></div>', unsafe_allow_html=True)
     #components.html(scroll_script, height=0, width=0)
 
-    
-    ## 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('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 +"? "