Delete type_text_v10.py

type_text_v10.py

@@ -1,126 +0,0 @@
-import streamlit as st
-import pandas as pd
-from io import StringIO
-import json
-import torch
-from transformers import pipeline # AutoTokenizer, AutoModelForCausalLM, AutoModelForTokenClassification
-from sentence_transformers import SentenceTransformer, util 
-
-import os
-os.getenv("HF_TOKEN")
-
-def get_device_map() -> str:
-    return 'cuda' if torch.cuda.is_available() else 'cpu'
-device = get_device_map()  # 'cpu'
-
-def on_click():
-    st.session_state.user_input = ""
-    
-#@st.cache
-def convert_df(df:pd.DataFrame):
-     return df.to_csv(index=False).encode('utf-8')
-
-#@st.cache
-def convert_json(df:pd.DataFrame):
-    result = df.to_json(orient="index")
-    parsed = json.loads(result)
-    json_string = json.dumps(parsed)
-    #st.json(json_string, expanded=True)
-    return json_string
-
-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")
-#st.button("Clear text", on_click=on_click)
-
-@st.cache_resource
-def load_model():
-    model = SentenceTransformer('all-MiniLM-L6-v2') # fastest
-    return model
-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')
-
-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_line = 0 # Imaging services chapter start, adjust as needed
-to_line = 10080 # Imaging services chapter end, adjust as needed
-nrows = to_line - from_line + 1
-skiprows = list(range(1,from_line - 1))
-df_SBS = pd.read_csv("SBS_V2_0/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()
-SBScorpus_embeddings = model.encode(SBScorpus)
-
-#my_model_results = pipeline("ner", model= "checkpoint-92")
-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_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 
-pipe = load_pipe()
-
-#pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B-Instruct", device_map=device,) # device_map="auto", torch_dtype=torch.bfloat16 
-
-dictA = {"Score": [], "SBS Code": [], "SBS Description V2.0": []}
-dfALL = pd.DataFrame.from_dict(dictA)
-
-if INTdesc_input is not None and createSBScodes_clicked == True: 
-    for i, result in enumerate(HF_model_results_displayed):
-        dictA.update({"Score": "%.4f" % result[0]["score"], "SBS Code": df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[0]["corpus_id"]],"SBS_Code_Hyphenated"].values[0], "SBS Description V2.0": SBScorpus[result[0]["corpus_id"]]})
-        dfALL = pd.concat([dfALL, pd.DataFrame([dictA])], ignore_index=True)
-        dictA.update({"Score": "%.4f" % result[1]["score"], "SBS Code": df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[1]["corpus_id"]],"SBS_Code_Hyphenated"].values[0], "SBS Description V2.0": SBScorpus[result[1]["corpus_id"]]})
-        dfALL = pd.concat([dfALL, pd.DataFrame([dictA])], ignore_index=True)
-        dictA.update({"Score": "%.4f" % result[2]["score"], "SBS Code": df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[2]["corpus_id"]],"SBS_Code_Hyphenated"].values[0], "SBS Description V2.0": SBScorpus[result[2]["corpus_id"]]})
-        dfALL = pd.concat([dfALL, pd.DataFrame([dictA])], ignore_index=True)
-        dictA.update({"Score": "%.4f" % result[3]["score"], "SBS Code": df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[3]["corpus_id"]],"SBS_Code_Hyphenated"].values[0], "SBS Description V2.0": SBScorpus[result[3]["corpus_id"]]})
-        dfALL = pd.concat([dfALL, pd.DataFrame([dictA])], ignore_index=True)
-        dictA.update({"Score": "%.4f" % result[4]["score"], "SBS Code": df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[4]["corpus_id"]],"SBS_Code_Hyphenated"].values[0], "SBS Description V2.0": SBScorpus[result[4]["corpus_id"]]})
-        dfALL = pd.concat([dfALL, pd.DataFrame([dictA])], ignore_index=True)
-        
-    st.dataframe(data=dfALL, hide_index=True)
-
-    display_format = "ask REASONING MODEL: Which, if any, of the above SBS descriptions corresponds best to " + INTdesc_input +"? " 
-    #st.write(display_format)
-    question = "Which one, if any, of the below Saudi Billing System descriptions A, B, C, D, or E corresponds best to " + INTdesc_input +"? " 
-    shortlist = [SBScorpus[result[0]["corpus_id"]], SBScorpus[result[1]["corpus_id"]], SBScorpus[result[2]["corpus_id"]], SBScorpus[result[3]["corpus_id"]], SBScorpus[result[4]["corpus_id"]]] 
-    prompt = question + " " +"A: "+ shortlist[0] + " " +"B: " + shortlist[1] + " " + "C: " + shortlist[2] + " " + "D: " + shortlist[3] + " " + "E: " + shortlist[4]
-    st.write(prompt)
-    
-    messages = [
-    {"role": "system", "content": "You are a knowledgable AI assistant who always answers truthfully and precisely!"},
-    {"role": "user", "content": prompt},
-    ]
-    outputs = pipe(
-        messages,
-        max_new_tokens=256,
-    )
-    st.write(outputs[0]["generated_text"][-1]["content"])
-    
-    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(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(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(dfALL), file_name= "results.json", mime='application/json',  key='json_b')