Update pages/type_text.py

pages/type_text.py

@@ -34,14 +34,14 @@ def convert_json(df:pd.DataFrame):
     #st.json(json_string, expanded=True)
     return json_string
 
-INTdesc_input = st.text_input("Type internal description and hit Enter", key="user_input") 
+INTdesc_input = st.text_input("Type internal description", key="user_input") 
 
 createSBScodes, right_column = st.columns(2)
 createSBScodes_clicked = createSBScodes.button("Map to SBS codes", key="user_createSBScodes")
 right_column.button("Reset", on_click=on_click)
 
 numMAPPINGS_input = 5
-#numMAPPINGS_input = st.text_input("Type number of mappings and hit Enter", key="user_input_numMAPPINGS")
+#numMAPPINGS_input = st.text_input("Type number of mappings", key="user_input_numMAPPINGS")
 #st.button("Clear text", on_click=on_click)
 
 @st.cache_resource
@@ -54,19 +54,23 @@ def load_model():
     return model
 model = load_model() 
 
-
+selected_chapters_dict = {0: [], 1:[], 2: 
 
 INTdesc_embedding = model.encode(INTdesc_input)
 
 # Semantic search, Compute cosine similarity between all pairs of SBS descriptions
 
-#df_SBS = pd.read_csv("SBS_V2_Table.csv", index_col="SBS_Code", usecols=["Long_Description"]) # na_values=['NA']
-#df_SBS = pd.read_csv("SBS_V2_Table.csv", usecols=["SBS_Code_Hyphenated","Long_Description"]) 
-from_row_index = 7725 # Imaging services chapter start, adjust as needed
-to_row_index = 8239 # Imaging services chapter end, adjust as needed
-nrows = to_row_index - from_row_index + 1
-skiprows = list(range(1,from_row_index - 1))
-df_SBS = pd.read_csv("SBS_V2_Table.csv", header=0, skip_blank_lines=False, skiprows=skiprows, nrows=nrows)
+#df_SBS = pd.read_csv("SBS/SBS_V2_Code_Table.csv", index_col="SBS_Code", usecols=["Long_Description"]) # na_values=['NA']
+#df_SBS = pd.read_csv("SBS/SBS_V2_Code_Table.csv", usecols=["SBS_Code_Hyphenated","Long_Description"]) 
+#from_row_index = 7725 # Imaging services chapter start, adjust as needed
+#to_row_index = 8239 # Imaging services chapter end, adjust as needed
+#nrows = to_row_index - from_row_index + 1
+#skiprows = list(range(1,from_row_index - 1))
+
+
+selected_rows_to_read = 
+df_SBS = pd.read_csv("SBS/SBS_V2_Code_Table.csv", skiprows = lambda x: x not in specific_rows)
+#df_SBS = pd.read_csv("SBS/SBS_V2_Code_Table.csv", header=0, skip_blank_lines=False, skiprows=skiprows, nrows=nrows)
 #st.write(df_SBS.head(5))
 
 SBScorpus = df_SBS['Long_Description'].values.tolist()
@@ -79,8 +83,8 @@ HF_model_results_displayed = HF_model_results_sorted[0:numMAPPINGS_input]
 
 @st.cache_resource
 def load_pipe():
-    #pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B-Instruct", device_map=device,) # device_map="auto", torch_dtype=torch.bfloat16 
-    pipe = pipeline("text-generation", model="Qwen/Qwen2-1.5B-Instruct", device_map=device,) # device_map="auto", torch_dtype="auto"
+    pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B-Instruct", device_map=device,) # device_map="auto", torch_dtype=torch.bfloat16 
+    #pipe = pipeline("text-generation", model="Qwen/Qwen2-1.5B-Instruct", device_map=device,) # device_map="auto", torch_dtype="auto"
     return pipe 
 pipe = load_pipe()