Delete app (1).py

app (1).py

@@ -1,142 +0,0 @@
-import streamlit as st
-import gradio as gr
-import shap
-import numpy as np
-import scipy as sp
-import torch
-import tensorflow as tf
-import transformers
-from transformers import pipeline
-from transformers import RobertaTokenizer, RobertaModel
-from transformers import AutoModelForSequenceClassification
-from transformers import TFAutoModelForSequenceClassification
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-
-import matplotlib.pyplot as plt
-import sys
-import csv
-
-csv.field_size_limit(sys.maxsize)
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-tokenizer = AutoTokenizer.from_pretrained("MarkAdamsMSBA24/ADRv2024")  
-model = AutoModelForSequenceClassification.from_pretrained("MarkAdamsMSBA24/ADRv2024").to(device)
-
-# build a pipeline object to do predictions
-pred = transformers.pipeline("text-classification", model=model, 
-                             tokenizer=tokenizer, return_all_scores=True)
-
-explainer = shap.Explainer(pred)
-
-##
-# classifier = transformers.pipeline("text-classification", model = "cross-encoder/qnli-electra-base")
-
-# def med_score(x):
-#     label = x['label']
-#     score_1 = x['score']
-#     return round(score_1,3)
-
-# def sym_score(x):
-#     label2sym= x['label']
-#     score_1sym = x['score']
-#     return round(score_1sym,3)
-
-ner_tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
-ner_model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
-
-ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
-#
-
-def adr_predict(x):
-    encoded_input = tokenizer(x, return_tensors='pt')
-    output = model(**encoded_input)
-    scores = output[0][0].detach().numpy()
-    scores = tf.nn.softmax(scores)
-   
-    shap_values = explainer([str(x).lower()])
-    # # Find the index of the class you want as the default reference (e.g., 'label_1')
-    # label_1_index = np.where(np.array(explainer.output_names) == 'label_1')[0][0]
-
-    # # Plot the SHAP values for a specific instance in your dataset (e.g., instance 0)
-    # shap.plots.text(shap_values[label_1_index][0])
-
-    local_plot = shap.plots.text(shap_values[0], display=False)
-
-    # med = med_score(classifier(x+str(", There is a medication."))[0])
-    # sym = sym_score(classifier(x+str(", There is a symptom."))[0])
-
-    res = ner_pipe(x)
-    
-    entity_colors = {
-    'Severity': 'red',
-    'Sign_symptom': 'green',
-    'Medication': 'lightblue',
-    'Age': 'yellow',
-    'Sex':'yellow',
-    'Diagnostic_procedure':'gray',
-    'Biological_structure':'silver'}
-
-    htext = ""
-    prev_end = 0
-
-    for entity in res:
-        start = entity['start']
-        end = entity['end']
-        word = entity['word'].replace("##", "")
-        color = entity_colors[entity['entity_group']]
-        
-        htext += f"{x[prev_end:start]}<mark style='background-color:{color};'>{word}</mark>"
-        prev_end = end
-
-    htext += x[prev_end:]
-   
-    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, local_plot,htext
-    # ,{"Contains Medication": float(med), "No Medications": float(1-med)} , {"Contains Symptoms": float(sym), "No Symptoms": float(1-sym)}
-
-
-def main(prob1):
-    text = str(prob1).lower()
-    obj = adr_predict(text)
-    return obj[0],obj[1],obj[2]
-
-title = "Welcome to **ADR Detector** 🪐"
-description1 = """This app takes text (up to a few sentences) and predicts to what extent the text describes severe (or non-severe) adverse reaction to medicaitons. Please do NOT use for medical diagnosis."""
-
-with gr.Blocks(title=title) as demo:
-    gr.Markdown(f"## {title}")
-    gr.Markdown(description1)
-    gr.Markdown("""---""")
-    prob1 = gr.Textbox(label="Enter Your Text Here:",lines=2, placeholder="Type it here ...")
-    submit_btn = gr.Button("Analyze")
-
-    with gr.Row():
-        
-        with gr.Column(visible=True) as output_col:
-            label = gr.Label(label = "Predicted Label")
-            
-
-        with gr.Column(visible=True) as output_col:
-            local_plot = gr.HTML(label = 'Shap:')
-            htext = gr.HTML(label="NER")
-            # med = gr.Label(label = "Contains Medication")
-            # sym = gr.Label(label = "Contains Symptoms")
-            
-    submit_btn.click(
-        main,
-        [prob1],
-        [label
-         ,local_plot, htext
-         # , med, sym
-        ], api_name="adr"
-    )
-    
-    with gr.Row():
-        gr.Markdown("### Click on any of the examples below to see how it works:")
-        gr.Examples([["A 35 year-old male had severe headache after taking Aspirin. The lab results were normal."],
-                     ["A 35 year-old female had minor pain in upper abdomen after taking Acetaminophen."]], 
-                    [prob1], [label,local_plot, htext
-         # , med, sym
-                             ], main, cache_examples=True)
-    
-demo.launch()