Delete type_text_v7.py

type_text_v7.py

@@ -1,150 +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 lmdeploy
-#import turbomind as tm 
-
-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
-
-#st.title("📘SBS mapper")
-
-INTdesc_input = st.text_input("Type internal description and hit Enter", 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():
-    #st.markdown("Sentence Transformer")
-    model = SentenceTransformer('all-MiniLM-L6-v2') # fastest
-    #st.success("Loaded model!")
-    #st.write("Turning on evaluation mode...")
-    #model.eval()
-    #st.write("Here's the model:")
-    return 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)
-
-# 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_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]
-
-model_id = "meta-llama/Llama-3.2-1B-Instruct"
-pipe = pipeline("text-generation", model=model_id, device_map=device,) # device_map="auto", torch_dtype=torch.bfloat16
-
-
-col1, col2, col3 = st.columns([1,1,2.5])
-col1.subheader("Score")
-col2.subheader("SBS code")
-col3.subheader("SBS description V2.0")
-
-dictA = {"Score": [], "SBS Code": [], "SBS Description V2.0": []}
-
-if INTdesc_input is not None and createSBScodes_clicked == True: 
-    #for i, result in enumerate(HF_model_results_displayed):
-    for result in HF_model_results_displayed:
-        with st.container():
-            col1.write("%.4f" % result[0]["score"])
-            col2.write(df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[0]["corpus_id"]],"SBS_Code_Hyphenated"].values[0])
-            col3.write(SBScorpus[result[0]["corpus_id"]])
-            #dictA["Score"].append("%.4f" % result[0]["score"]), dictA["SBS Code"].append(df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[0]["corpus_id"]],"SBS_Code_Hyphenated"].values[0]), dictA["SBS Description V2.0"].append(SBScorpus[result[0]["corpus_id"]])
-
-            col1.write("%.4f" % result[1]["score"])
-            col2.write(df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[1]["corpus_id"]],"SBS_Code_Hyphenated"].values[0])
-            col3.write(SBScorpus[result[1]["corpus_id"]])
-            #dictA["Score"].append("%.4f" % result[1]["score"]), dictA["SBS Code"].append(df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[1]["corpus_id"]],"SBS_Code_Hyphenated"].values[0]), dictA["SBS Description V2.0"].append(SBScorpus[result[1]["corpus_id"]])
-            
-            col1.write("%.4f" % result[2]["score"])
-            col2.write(df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[2]["corpus_id"]],"SBS_Code_Hyphenated"].values[0])
-            col3.write(SBScorpus[result[2]["corpus_id"]])
-            #dictA["Score"].append("%.4f" % result[2]["score"]), dictA["SBS Code"].append(df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[2]["corpus_id"]],"SBS_Code_Hyphenated"].values[0]), dictA["SBS Description V2.0"].append(SBScorpus[result[2]["corpus_id"]])
-            
-            col1.write("%.4f" % result[3]["score"])
-            col2.write(df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[3]["corpus_id"]],"SBS_Code_Hyphenated"].values[0])
-            col3.write(SBScorpus[result[3]["corpus_id"]])
-            dictA["Score"].append("%.4f" % result[3]["score"]), dictA["SBS Code"].append(df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[3]["corpus_id"]],"SBS_Code_Hyphenated"].values[0]), dictA["SBS Description V2.0"].append(SBScorpus[result[3]["corpus_id"]])
-            
-            col1.write("%.4f" % result[4]["score"])
-            col2.write(df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[4]["corpus_id"]],"SBS_Code_Hyphenated"].values[0])
-            col3.write(SBScorpus[result[4]["corpus_id"]])
-            #dictA["Score"].append("%.4f" % result[4]["score"]), dictA["SBS Code"].append(df_SBS.loc[df_SBS["Long_Description"] == SBScorpus[result[4]["corpus_id"]],"SBS_Code_Hyphenated"].values[0]), dictA["SBS Description V2.0"].append(SBScorpus[result[4]["corpus_id"]])
-
-            dfA = pd.DataFrame.from_dict(dictA) 
-
-    display_format = "ask REASONING MODEL: Which, if any, of the above Saudi Billing System descriptions corresponds best to " + INTdesc_input +"? " 
-    st.write(display_format)
-    question = "Which, if any, of the below Saudi Billing System descriptions 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 + " " + shortlist[0] + " " + shortlist[1] + " " + shortlist[2] + " " + shortlist[3] + " " + 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(dfA), 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')
-    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')