Signed-off-by: ZHOU-henry <[email protected]>

App.py

@@ -1,201 +0,0 @@
-# Import and class names setup
-import gradio as gr
-import os
-import torch
-import random
-#import nltk_utils
-import pandas as pd
-from sklearn.model_selection import train_test_split
-import time
-
-#from model import RNN_model
-from timeit import default_timer as timer
-from typing import Tuple, Dict
-
-# Import data
-df= pd.read_csv('Symptom2Disease.csv')
-df.drop('Unnamed: 0', axis= 1, inplace= True)
-
-# Preprocess data
-df.drop_duplicates(inplace= True)
-train_data, test_data= train_test_split(df, test_size=0.15, random_state=42 )
-
-# Setup class names
-class_names= {0: 'Acne',
-              1: 'Arthritis',
-              2: 'Bronchial Asthma',
-              3: 'Cervical spondylosis',
-              4: 'Chicken pox',
-              5: 'Common Cold',
-              6: 'Dengue',
-              7: 'Dimorphic Hemorrhoids',
-              8: 'Fungal infection',
-              9: 'Hypertension',
-              10: 'Impetigo',
-              11: 'Jaundice',
-              12: 'Malaria',
-              13: 'Migraine',
-              14: 'Pneumonia',
-              15: 'Psoriasis',
-              16: 'Typhoid',
-              17: 'Varicose Veins',
-              18: 'allergy',
-              19: 'diabetes',
-              20: 'drug reaction',
-              21: 'gastroesophageal reflux disease',
-              22: 'peptic ulcer disease',
-              23: 'urinary tract infection'
-              }
-
-#vectorizer= nltk_utils.vectorizer()
-#vectorizer.fit(train_data.text)
-
-
-
-# Model and transforms preparation
-#model= RNN_model()
-# Load state dict
-#model.load_state_dict(torch.load(
-#    f= 'pretrained_symtom_to_disease_model.pth',
-#    map_location= torch.device('cpu'))
-# Disease Advice
-disease_advice = {
-    'Acne': "Maintain a proper skincare routine, avoid excessive touching of the affected areas, and consider using over-the-counter topical treatments. If severe, consult a dermatologist.",
-    'Arthritis': "Stay active with gentle exercises, manage weight, and consider pain-relief strategies like hot/cold therapy. Consult a rheumatologist for tailored guidance.",
-    'Bronchial Asthma': "Follow prescribed inhaler and medication regimen, avoid triggers like smoke and allergens, and have an asthma action plan. Regular check-ups with a pulmonologist are important.",
-    'Cervical spondylosis': "Maintain good posture, do neck exercises, and use ergonomic support. Physical therapy and pain management techniques might be helpful.",
-    'Chicken pox': "Rest, maintain hygiene, and avoid scratching. Consult a doctor for appropriate antiviral treatment.",
-    'Common Cold': "Get plenty of rest, stay hydrated, and consider over-the-counter remedies for symptom relief. Seek medical attention if symptoms worsen or last long.",
-    'Dengue': "Stay hydrated, rest, and manage fever with acetaminophen. Seek medical care promptly, as dengue can escalate quickly.",
-    'Dimorphic Hemorrhoids': "Follow a high-fiber diet, maintain good hygiene, and consider stool softeners. Consult a doctor if symptoms persist.",
-    'Fungal infection': "Keep the affected area clean and dry, use antifungal creams, and avoid sharing personal items. Consult a dermatologist if it persists.",
-    'Hypertension': "Follow a balanced diet, exercise regularly, reduce salt intake, and take prescribed medications. Regular check-ups with a healthcare provider are important.",
-    'Impetigo': "Keep the affected area clean, use prescribed antibiotics, and avoid close contact. Consult a doctor for proper treatment.",
-    'Jaundice': "Get plenty of rest, maintain hydration, and follow a doctor's advice for diet and medications. Regular monitoring is important.",
-    'Malaria': "Take prescribed antimalarial medications, rest, and manage fever. Seek medical attention for severe cases.",
-    'Migraine': "Identify triggers, manage stress, and consider pain-relief medications. Consult a neurologist for personalized management.",
-    'Pneumonia': "Follow prescribed antibiotics, rest, stay hydrated, and monitor symptoms. Seek immediate medical attention for severe cases.",
-    'Psoriasis': "Moisturize, use prescribed creams, and avoid triggers. Consult a dermatologist for effective management.",
-    'Typhoid': "Take prescribed antibiotics, rest, and stay hydrated. Dietary precautions are important. Consult a doctor for proper treatment.",
-    'Varicose Veins': "Elevate legs, exercise regularly, and wear compression stockings. Consult a vascular specialist for evaluation and treatment options.",
-    'allergy': "Identify triggers, manage exposure, and consider antihistamines. Consult an allergist for comprehensive management.",
-    'diabetes': "Follow a balanced diet, exercise, monitor blood sugar levels, and take prescribed medications. Regular visits to an endocrinologist are essential.",
-    'drug reaction': "Discontinue the suspected medication, seek medical attention if symptoms are severe, and inform healthcare providers about the reaction.",
-    'gastroesophageal reflux disease': "Follow dietary changes, avoid large meals, and consider medications. Consult a doctor for personalized management.",
-    'peptic ulcer disease': "Avoid spicy and acidic foods, take prescribed medications, and manage stress. Consult a gastroenterologist for guidance.",
-    'urinary tract infection': "Stay hydrated, take prescribed antibiotics, and maintain good hygiene. Consult a doctor for appropriate treatment."
-}
-
-howto= """Welcome to the <b>Medical Chatbot</b>, powered by Gradio.
-Currently, the chatbot can WELCOME YOU, PREDICT DISEASE based on your symptoms and SUGGEST POSSIBLE SOLUTIONS AND RECOMENDATIONS, and BID YOU FAREWELL.
-<br><br>
-Here's a quick guide to get you started:<br><br>
-<b>How to Start:</b> Simply type your messages in the textbox to chat with the Chatbot and press enter!<br><br>
-The bot will respond based on the best possible answers to your messages. For now, let's keep it SIMPLE as I'm working hard to enhance its capabilities in the future.
-
-"""
-
-
-# Create the gradio demo
-with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""") as demo:
-  gr.HTML('<h1 align="center">Medical Chatbot: Your Virtual Health Guide 🌟🏥🤖"</h1>')
-  gr.HTML('<h3 align="center">To know more about this project click, <a href="https://github.com/Monsurat-Onabajo/Medical_chatbot" target="_blank">Here</a>')
-  with gr.Accordion("Follow these Steps to use the Gradio WebUI", open=True):
-      gr.HTML(howto)
-  chatbot = gr.Chatbot()
-  msg = gr.Textbox()
-  clear = gr.ClearButton([msg, chatbot])
-
-  def respond(message, chat_history):
-    # Random greetings in list format
-    greetings = [
-        "hello!",'hello', 'hii !', 'hi', "hi there!",  "hi there!", "heyy", 'good morning', 'good afternoon', 'good evening'
-        "hey", "how are you", "how are you?", "how is it going", "how is it going?",
-        "what's up?", "how are you?",
-        "hey, how are you?", "what is popping"
-        "good to see you!", "howdy!",
-        "hi, nice to meet you.", "hiya!",
-        "hi", "hi, what's new?",
-        "hey, how's your day?", "hi, how have you been?", "greetings",
-        ]
-    # Random Greetings responses
-    responses = [
-        "Thank you for using our medical chatbot. Please provide the symptoms you're experiencing, and I'll do my best to predict the possible disease.",
-        "Hello! I'm here to help you with medical predictions based on your symptoms. Please describe your symptoms in as much detail as possible.",
-        "Greetings! I am a specialized medical chatbot trained to predict potential diseases based on the symptoms you provide. Kindly list your symptoms explicitly.",
-        "Welcome to the medical chatbot. To assist you accurately, please share your symptoms in explicit detail.",
-        "Hi there! I'm a medical chatbot specialized in analyzing symptoms to suggest possible diseases. Please provide your symptoms explicitly.",
-        "Hey! I'm your medical chatbot. Describe your symptoms with as much detail as you can, and I'll generate potential disease predictions.",
-        "How can I assist you today? I'm a medical chatbot trained to predict diseases based on symptoms. Please be explicit while describing your symptoms.",
-        "Hello! I'm a medical chatbot capable of predicting diseases based on the symptoms you provide. Your explicit symptom description will help me assist you better.",
-        "Greetings! I'm here to help with medical predictions. Describe your symptoms explicitly, and I'll offer insights into potential diseases.",
-        "Hi, I'm the medical chatbot. I've been trained to predict diseases from symptoms. The more explicit you are about your symptoms, the better I can assist you.",
-        "Hi, I specialize in medical predictions based on symptoms. Kindly provide detailed symptoms for accurate disease predictions.",
-        "Hello! I'm a medical chatbot with expertise in predicting diseases from symptoms. Please describe your symptoms explicitly to receive accurate insights.",
-        ]
-    # Random goodbyes
-    goodbyes = [
-        "farewell!",'bye', 'goodbye','good-bye', 'good bye', 'bye', 'thank you', 'later', "take care!",
-        "see you later!", 'see you', 'see ya', 'see-you', 'thanks', 'thank', 'bye bye', 'byebye'
-        "catch you on the flip side!", "adios!",
-        "goodbye for now!", "till we meet again!",
-        "so long!", "hasta la vista!",
-        "bye-bye!", "keep in touch!",
-        "toodles!", "ciao!",
-        "later, gator!", "stay safe and goodbye!",
-        "peace out!", "until next time!", "off I go!",
-        ]
-    # Random Goodbyes responses
-    goodbye_replies = [
-        "Take care of yourself! If you have more questions, don't hesitate to reach out.",
-        "Stay well! Remember, I'm here if you need further medical advice.",
-        "Goodbye for now! Don't hesitate to return if you need more information in the future.",
-        "Wishing you good health ahead! Feel free to come back if you have more concerns.",
-        "Farewell! If you have more symptoms or questions, don't hesitate to consult again.",
-        "Take care and stay informed about your health. Feel free to chat anytime.",
-        "Bye for now! Remember, your well-being is a priority. Don't hesitate to ask if needed.",
-        "Have a great day ahead! If you need medical guidance later on, I'll be here.",
-        "Stay well and take it easy! Reach out if you need more medical insights.",
-        "Until next time! Prioritize your health and reach out if you need assistance.",
-        "Goodbye! Your health matters. Feel free to return if you have more health-related queries.",
-        "Stay healthy and stay curious about your health! If you need more info, just ask.",
-        "Wishing you wellness on your journey! If you have more questions, I'm here to help.",
-        "Take care and remember, your health is important. Don't hesitate to reach out if needed.",
-        "Goodbye for now! Stay informed and feel free to consult if you require medical advice.",
-        "Stay well and stay proactive about your health! If you have more queries, feel free to ask.",
-        "Farewell! Remember, I'm here whenever you need reliable medical information.",
-        "Bye for now! Stay vigilant about your health and don't hesitate to return if necessary.",
-        "Take care and keep your well-being a priority! Reach out if you have more health questions.",
-        "Wishing you good health ahead! Don't hesitate to chat if you need medical insights.",
-        "Goodbye! Stay well and remember, I'm here to assist you with medical queries.",
-    ]
-
-    # Create couple of if-else statements to capture/mimick peoples's Interaction
-  
-    if message.lower() in greetings:
-      bot_message= random.choice(responses)
-    elif message.lower() in goodbyes:
-      bot_message= random.choice(goodbye_replies)
-    else: 
-      bot_message= random.choice(goodbye_replies)
-    '''
-    else:
-      transform_text= vectorizer.transform([message])
-      transform_text= torch.tensor(transform_text.toarray()).to(torch.float32)
-      model.eval()
-      with torch.inference_mode():
-        y_logits=model(transform_text)
-        pred_prob= torch.argmax(torch.softmax(y_logits, dim=1), dim=1)
-   
-      test_pred= class_names[pred_prob.item()] 
-      bot_message = f' Based on your symptoms, I believe you are having {test_pred} and I would advice you {disease_advice[test_pred]}'
-    chat_history.append((message, bot_message))
-    time.sleep(2)
-    return "", chat_history
-    '''
-  msg.submit(respond, [msg, chatbot], [msg, chatbot])
-  
-
-# Launch the demo
-demo.launch()
-

model.py

@@ -1,17 +0,0 @@
-import torch
-from torch import nn
-
-class RNN_model(nn.Module):
-  def __init__(self):
-    super().__init__()
-
-    self.rnn= nn.RNN(input_size=1080, hidden_size=240,num_layers=1, nonlinearity= 'relu', bias= True)
-    self.output= nn.Linear(in_features=240, out_features=24)
-
-  def forward(self, x):
-    y, hidden= self.rnn(x)
-    #print(y.shape)
-    #print(hidden.shape)
-    x= self.output(y)
-
-    return(x)

nltk_u.py

@@ -1,24 +0,0 @@
-# Import Libraries
-import nltk
-from nltk.tokenize import word_tokenize
-nltk.download('punkt')
-from sklearn.feature_extraction.text import TfidfVectorizer
-from nltk.stem import SnowballStemmer
-stemmer= SnowballStemmer(language= 'english')
-from nltk.corpus import stopwords
-nltk.download('stopwords')
-
-# Tokenize text i.e make all text be in a list format e.g "I am sick" = ['i', 'am', 'sick']
-def tokenize(text):
-  return [stemmer.stem(token) for token in word_tokenize(text)]
-
-# Create stopwords to reduce noise in data
-english_stopwords= stopwords.words('english')
-
-# Create a vectosizer to learn all words in order to convert them into numbers
-def vectorizer():
-    vectorizer= TfidfVectorizer(tokenizer=tokenize,
-                                stop_words=english_stopwords,
-                                )
-    return vectorizer
-

pretrained_symtom_to_disease_model.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d182949722a706bed0e7c6319bb893eccc31f8d2b502d023750e0beafd5da8fe
-size 1294703