Upload app.py

app.py

@@ -3,13 +3,14 @@ import gradio as gr
 import os
 import torch
 import random
-#import nltk_u
+import nltk_u
 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
+from torch import nn
 ################################################################################
 import argparse
 import numpy as np
@@ -24,8 +25,10 @@ from modeling_phi import PhiForCausalLM
 from tokenization_codegen import CodeGenTokenizer
 from transformers import PhiForCausalLM, AutoTokenizer, AutoModelForCausalLM
 ################################################################################
+
 parser = argparse.ArgumentParser()
 #############################################################################################################################
+
 parser.add_argument('--device_id', type=str, default="0")
 parser.add_argument('--model', type=str, default="microsoft/phi-2", help="") ## /phi-1.5
 parser.add_argument('--embedder', type=str, default="BAAI/bge-small-en-v1.5") ## /bge-small-en-v1.5  # bge-m3
@@ -38,7 +41,6 @@ parser.add_argument('--prompt_type', type=str, default="v2.0", help="")
 parser.add_argument('--top_k', type=str2bool, default=True, help="")
 #############################################################################################################################
 args = parser.parse_args()
-
 if torch.cuda.is_available():
     device = "cuda"
     print(f'################################################################# device: {device}#################################################################')
@@ -55,7 +57,95 @@ def get_model(base_model: str = "bigcode/starcoder",):
     model.config.pad_token_id = tokenizer.pad_token_id
     model.eval()
     return tokenizer, model
+
+
+class RNN_model(nn.Module):
+  def __init__(self):
+    super().__init__()
+
+    self.rnn= nn.GRU(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)
 ################################################################################
+# 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_u.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."
+}
 
 '''
 # Import data
@@ -70,18 +160,19 @@ train_data, test_data= train_test_split(df, test_size=0.15, random_state=42 )
 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.
 <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."""
+The bot will respond based on the best possible answers to your messages.
 
+"""
 # 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: ARIN 7102')
-  #gr.HTML('<h3 align="center">To know more about this project')
-  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])
-  '''
+    gr.HTML('<h1 align="center">Medical Chatbot: ARIN 7102')
+    #gr.HTML('<h3 align="center">To know more about this project')
+    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):
     # Create couple of if-else statements to capture/mimick peoples's Interaction
     embedder = SentenceTransformer(args.embedder, device=device)
@@ -91,112 +182,117 @@ with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
     chat_history.append((message, bot_message))
     time.sleep(2)
     return "", chat_history
-  '''
-  def respond(message, chat_history, base_model = "microsoft/phi-2", device=device): # "meta-llama/Meta-Llama-3-70B"
-   if base_model != "microsoft/phi-2":
-    # 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!",
+'''
+def respond(message, chat_history, base_model = "microsoft/phi-2", device=device): # "meta-llama/Meta-Llama-3-70B"
+    if base_model != "microsoft/phi-2":
+            # 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.",
         ]
-    # 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:
+            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]}'
+    else:
+            # define the model and tokenizer.
+            # model = PhiForCausalLM.from_pretrained(base_model)
+            model = AutoModelForCausalLM.from_pretrained(base_model)
+            tokenizer = AutoTokenizer.from_pretrained(base_model)
 
-    # 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: 
-        inputs = preprocess(message)
-        bot_message = f"Based on your symptoms, I believe you may have {get_prediction(inputs)}."
-   else:
-        # define the model and tokenizer.
-        # model = PhiForCausalLM.from_pretrained(base_model)
-        model = AutoModelForCausalLM.from_pretrained(base_model)
-        tokenizer = AutoTokenizer.from_pretrained(base_model)
-
-        # feel free to change the prompt to your liking.
-        #prompt = f"Patient: coercive spondylitis, pain in the lumbosacral area when turning over during sleep at night, no pain in any other part of the body.  
-        #/n Doctor: It shouldn't be a problem, but it's better to upload the images. /n Patient: {message} /n Doctor:"
-        output_termination = "\nOutput:"
-        prompt = f"Instruct: {message}{output_termination}"
-        # apply the tokenizer.
-        tokens = tokenizer(prompt, return_tensors="pt", return_attention_mask=False)
-        #tokens = tokens.to(device)
-        #eos_token_id = tokenizer.eos_token_id
-        # use the model to generate new tokens.
-        generated_output = model.generate(**tokens, use_cache=True, max_new_tokens=100, eos_token_id=50256, pad_token_id=50256)
-
-        # Find the position of "Output:" and extract the text after it
-        generated_text = tokenizer.batch_decode(generated_output)[0]
-        # Split the text at "Output:" and take the second part
-        split_text = generated_text.split("Output:", 1)
-        bot_message = split_text[1].strip() if len(split_text) > 1 else ""
-        bot_message = bot_message.replace("<|endoftext|>", "").strip()
-        chat_history.append((message, bot_message))
-        time.sleep(2)
-        return "", chat_history
+            # feel free to change the prompt to your liking.
+            #prompt = f"Patient: coercive spondylitis, pain in the lumbosacral area when turning over during sleep at night, no pain in any other part of the body.  
+            #/n Doctor: It shouldn't be a problem, but it's better to upload the images. /n Patient: {message} /n Doctor:"
+            output_termination = "\nOutput:"
+            prompt = f"Instruct: {message}{output_termination}"
+            # apply the tokenizer.
+            tokens = tokenizer(prompt, return_tensors="pt", return_attention_mask=False)
+            #tokens = tokens.to(device)
+            #eos_token_id = tokenizer.eos_token_id
+            # use the model to generate new tokens.
+            generated_output = model.generate(**tokens, use_cache=True, max_new_tokens=500, eos_token_id=50256, pad_token_id=50256)
+
+            # Find the position of "Output:" and extract the text after it
+            generated_text = tokenizer.batch_decode(generated_output)[0]
+            # Split the text at "Output:" and take the second part
+            split_text = generated_text.split("Output:", 1)
+            bot_message = split_text[1].strip() if len(split_text) > 1 else ""
+            bot_message = bot_message.replace("<|endoftext|>", "").strip()
+    chat_history.append((message, bot_message))
+    time.sleep(2)
+    return "", chat_history
     #return bot_message
-       
-  msg.submit(respond, [msg, chatbot], [msg, chatbot])
-  
+
+msg.submit(respond, [msg, chatbot], [msg, chatbot])
 
 # Launch the demo
 demo.launch()