Update pages/type_text.py

pages/type_text.py

@@ -46,12 +46,13 @@ numMAPPINGS_input = 5
 def load_model():
     model = SentenceTransformer('all-MiniLM-L6-v2') # fastest
     return model
+load_model() 
+
 model = SentenceTransformer('all-MiniLM-L6-v2') # fastest
 #model = SentenceTransformer('all-mpnet-base-v2') # best performance
 #model = SentenceTransformers('all-distilroberta-v1')
 #model = SentenceTransformer('sentence-transformers/msmarco-bert-base-dot-v5') 
 #model = SentenceTransformer('clips/mfaq')
-load_model()
 
 INTdesc_embedding = model.encode(INTdesc_input)
 
@@ -74,12 +75,13 @@ HF_model_results = util.semantic_search(INTdesc_embedding, SBScorpus_embeddings)
 HF_model_results_sorted = sorted(HF_model_results, key=lambda x: x[1], reverse=True)
 HF_model_results_displayed = HF_model_results_sorted[0:numMAPPINGS_input]
 
-@st.cache_resource
-def load_model_pipe():
-    pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B-Instruct", device_map=device,) # device_map="auto", torch_dtype=torch.bfloat16 
-    return pipe 
+#@st.cache_resource
+#def load_model_pipe():
+#    pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B-Instruct", device_map=device,) # device_map="auto", torch_dtype=torch.bfloat16 
+#    return pipe 
+#load_model_pipe()
+
 pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B-Instruct", device_map=device,) # device_map="auto", torch_dtype=torch.bfloat16 
-load_model_pipe()
 
 dictA = {"Score": [], "SBS Code": [], "SBS Description V2.0": []}
 dfALL = pd.DataFrame.from_dict(dictA)
@@ -120,10 +122,10 @@ if INTdesc_input is not None and createSBScodes_clicked == True:
     bs, b1, b2, b3, bLast = st.columns([0.75, 1.5, 1.5, 1.5, 0.75])
     with b1:
         #csvbutton = download_button(results, "results.csv", "📥 Download .csv")
-        csvbutton = st.download_button(label="📥 Download .csv", data=convert_df(dfA), file_name= "results.csv", mime='text/csv', key='csv_b')
+        csvbutton = st.download_button(label="📥 Download .csv", data=convert_df(dfALL), file_name= "results.csv", mime='text/csv', key='csv_b')
     with b2:
         #textbutton = download_button(results, "results.txt", "📥 Download .txt")
-        textbutton = st.download_button(label="📥 Download .txt", data=convert_df(dfA), file_name= "results.text", mime='text/plain',  key='text_b')
+        textbutton = st.download_button(label="📥 Download .txt", data=convert_df(dfALL), file_name= "results.text", mime='text/plain',  key='text_b')
     with b3:
         #jsonbutton = download_button(results, "results.json", "📥 Download .json")
-        jsonbutton = st.download_button(label="📥 Download .json", data=convert_json(dfA), file_name= "results.json", mime='application/json',  key='json_b')
+        jsonbutton = st.download_button(label="📥 Download .json", data=convert_json(dfALL), file_name= "results.json", mime='application/json',  key='json_b')