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bot.py

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+import nltk
+import random
+import numpy as np
+import json
+import pickle
+from nltk.stem import WordNetLemmatizer
+from tensorflow.keras.models import load_model
+
+lemmatizer = WordNetLemmatizer()
+
+with open('intents.json') as json_file:
+    intents = json.load(json_file)
+
+words = pickle.load(open('words.pkl', 'rb'))
+classes = pickle.load(open('classes.pkl', 'rb'))
+model = load_model('chatbotmodel.h5')
+
+def clean_up_sentence(sentence):
+    sentence_words = nltk.word_tokenize(sentence)
+    sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
+    return sentence_words
+
+def bag_of_words(sentence):
+    sentence_words = clean_up_sentence(sentence)
+    bag = [0] * len(words)
+    for w in sentence_words:
+        for i, word in enumerate(words):
+            if word == w:
+                bag[i] = 1
+    return np.array(bag)
+
+def predict_class(sentence):
+    bow = bag_of_words(sentence)
+    res = model.predict(np.array([bow]))[0]
+    ERROR_THRESHOLD = 0.25
+    results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
+
+    results.sort(key=lambda x: x[1], reverse=True)
+    return_list = []
+    for r in results:
+        return_list.append({'intent': classes[r[0]], 'probability': str(r[1])})
+    return return_list
+
+def get_response(intents_list, intents_json):
+    tag = intents_list[0]['intent']
+    list_of_intents = intents_json['intents']
+    for i in list_of_intents:
+        if i['tag'] == tag:
+            result = random.choice(i['responses'])
+            break
+    return result
+
+def chat(text):
+    ints = predict_class(text)
+    res = get_response(ints, intents)
+    return res
+
+print("GO! BOT IS RUNNING")

chatbotmodel.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0703f08768ffca2457cf3690e9a8f18ea2dec3127d985af5f45b4d9eb5fa5ebc
+size 242776

classes.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e1f79d3640efc82efba391235ce3272f75910e2864507eed926d6093c10ac2c
+size 276

intents.json

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+{"intents":
+[
+            {
+             "tag":"greetings",
+             "patterns": ["hello","hey","hi","good day","greetings","what's up?","how is it going"],
+             "responses":["hello","hey!","what can i do for you?"]
+            },
+            {
+             "tag":"goodbye",
+             "patterns": ["cya","see you later","goodbye","have a good day","bye","cao","see ya"],
+             "responses":["have a nice day","goodbye"]
+            },
+            {
+             "tag":"age",
+             "patterns": ["how old","how old are you?","what is your age","how old are you","age?"],
+             "responses":["I get reborn after every compilation","hey!","my owners are averagely 20 years!"]
+            },
+            {
+             "tag":"name",
+             "patterns": ["what is your name","what should i call you","what's your name?","who are you?","can you tell me your name"],
+             "responses":["you can call me Medbot!","i am Medbot!","i am Medbot your medical assistant"]
+            },
+            {
+             "tag":"common cold symptoms",
+             "patterns": ["Runny or stuffy nose",
+             "Sore throat",
+             "Cough",
+             "Congestion",
+             "Slight body aches or a mild headache",
+             "Sneezing",
+             "Low-grade fever",
+             "Generally feeling unwell (malaise)"],
+             "responses":["It seems that you are suffering from common cold"]
+            },
+            {
+             "tag":"fever symptoms",
+             "patterns":["Sweating",
+             "Chills and shivering",
+             "Headache",
+             "Muscle aches",
+             "Loss of appetite",
+             "Irritability",
+             "Dehydration",
+             "General weakness"],
+             "responses":["It seems that you are suffering from fever"]
+            },
+            {
+             "tag":"Diabetes symptoms",
+             "patterns":["increased hunger",
+             "increased thirst",
+             "weight loss",
+             "frequent urination",
+             "blurry vision",
+             "extreme fatigue"],
+             "responses":["It seems that you are suffering from Diabetes"]
+            },
+            {
+             "tag":"Depression symptoms",
+             "patterns":["Hopeless outlook",
+             "Lost interest",
+             "Increased fatigue",
+             "sleep problem",
+             "Anxiety",
+             "change in weight",
+             "Looking at death"],
+             "responses":["It seem that you are suffering from depression"]
+            },
+            {
+             "tag":"Asthma symptoms",
+             "patterns":["coughing",
+             "tightness in the chest",
+             "shortness of breath",
+             "difficulty talking",
+             "panic",
+             "fatigue"],
+             "responses":["It seem that you are suffering from Asthma"]
+            },
+            {
+             "tag":"common cold prevention",
+             "patterns":["What medicines can I buy to help me with my common cold?","tell me some prevention method from common cold","What should I eat or drink if i am suffering from common cold?","How can I keep from getting a cold or the flu?"],
+             "responses":["medicines you can consume : Dextromethorphan,Decongestant,Diphenhydramine,Crocin Cold & Flu Max, preventions that you must follow :Wash your hands,Avoid touching your face,Clean frequently used surfaces,Use hand sanitizers,SUGGESTED FOODS ARE:Garlic,Vitamin C–containing fruits,Leafy greens,Broccoli,Oatmeal,Spices,Chicken Soup"]
+            },
+            {
+             "tag":"fever prevention",
+             "patterns":["What medicines can I buy to help me with my fever?","tell me some prevention method from fever","What should I eat or drink if i am suffering from fever?","How can I keep from getting a fever?"],
+             "responses":["medicines you can consume : acetaminophen ,ibuprofen,aspirin,Crocin Cold & Flu Max, prevention that you must follow :Wash your hands,Cover your mouth when you cough and your nose when you sneeze,Clean frequently used surfaces,Avoid sharing cups, glasses, and eating utensils with other people.,SUGGESTED FOODS ARE:Garlic,Vitamin C–containing fruits,Leafy greens,Broccoli,Oatmeal,Spices,Chicken Soup"]
+            },
+            {
+             "tag":"diabetes prevention",
+             "patterns":["What medicines can I buy to help me with my diabetes?","tell me some prevention method from diabetes","What should I eat or drink if i am suffering from diabetes?","How can I keep from getting diabetes?"],
+             "responses":["medicines you can consume : Insulin ,Amylinomimetic drug,Dipeptidyl peptidase-4 (DPP-4) inhibitor, prevention that you must follow :Cut Sugar and Refined Carbs From Your Diet,Work Out Regularly,Drink Water as Your Primary Beverage,Lose Weight If You’re Overweight or Obese,Quit Smoking, Follow a Very-Low-Carb Diet,Watch Portion Sizes,SUGGESTED FOODS ARE:Leafy greens,Avocados,Eggs"]
+            },
+            {
+             "tag":"depression prevention",
+             "patterns":["What medicines can I buy to help me with my depression?","tell me some prevention method from depression","What should I eat or drink if i am suffering from depression?","How can I keep from getting depression?"],
+             "responses":["medicines you can consume :  brexpiprazole, quetiapine,olanzapine, prevention that you must follow :Exercise regularly,Cut back on social media time,Drink Water as Your Primary Beverage,Build strong relationships,Minimize your daily choices, Follow a Very-Low-Carb Diet,SUGGESTED FOODS ARE:Get Enough Vitamin D,Include Omega-3 Fatty Acids,Beans and legumes"]
+            },
+            {
+             "tag":"asthma prevention",
+             "patterns":["What medicines can I buy to help me with my asthma?","tell me some prevention method from asthma","What should I eat or drink if i am suffering from asthma?","How can I keep from getting asthma?"],
+             "responses":["medicines you can consume : epinephrine,anticholinergic,Proair HFA, prevention that you must follow : Identify Asthma Triggers, Stay Away From Allergens,Avoid Smoke of Any Type,SUGGESTED FOODS ARE:carrots,juice,eggs,broccoli,cantaloupe,milk"]
+            },
+            {
+             "tag":"Consultation",
+             "patterns": ["who should i contact for consultation?","is there any doctor available?","can you give me some suggestions for doctor consultations?","can you set up a meeting with a doctor for consultation?","is there any doctor available for consultation"],
+             "responses":["You can contact various doctors here for any kind of consultation: 1. https://www.1mg.com/online-doctor-consultation, 2. https://www.tatahealth.com/online-doctor-consultation/general-physician, 3. https://www.doconline.com/, or you can pay a visit to your local area doctor or family doctor."]
+            }
+]}
+        

train.py

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+import nltk
+import random
+import numpy as np
+import json
+import pickle
+from nltk.stem import WordNetLemmatizer
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout
+from tensorflow.keras.optimizers import SGD
+
+lemmatizer = WordNetLemmatizer()
+
+# Load the intents file
+with open('intents.json') as json_file:
+    intents = json.load(json_file)
+
+# Initialize lists
+words = []
+classes = []
+documents = []
+ignore_words = ['?', '!']
+
+# Process the intents
+for intent in intents['intents']:
+    for pattern in intent['patterns']:
+        word_list = nltk.word_tokenize(pattern)
+        words.extend(word_list)
+        documents.append((word_list, intent['tag']))
+        if intent['tag'] not in classes:
+            classes.append(intent['tag'])
+
+words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
+words = sorted(list(set(words)))
+classes = sorted(list(set(classes)))
+
+pickle.dump(words, open('words.pkl', 'wb'))
+pickle.dump(classes, open('classes.pkl', 'wb'))
+
+training = []
+output_empty = [0] * len(classes)
+
+# Debugging: Print lengths of words and classes
+print(f'Number of words: {len(words)}')
+print(f'Number of classes: {len(classes)}')
+
+for doc in documents:
+    bag = []
+    pattern_words = doc[0]
+    pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]
+    for word in words:
+        bag.append(1) if word in pattern_words else bag.append(0)
+    output_row = list(output_empty)
+    output_row[classes.index(doc[1])] = 1
+    training.append([bag, output_row])
+
+# Debugging: Check for inconsistencies in training data
+for i, t in enumerate(training):
+    if len(t[0]) != len(words):
+        print(f'Inconsistent length in training data at index {i}: {len(t[0])} != {len(words)}')
+
+random.shuffle(training)
+training = np.array(training, dtype=object)
+
+# Debugging: Print shape of training data
+print(f'Training data shape: {training.shape}')
+
+train_x = list(training[:, 0])
+train_y = list(training[:, 1])
+
+# Debugging: Print shapes of train_x and train_y
+print(f'Shape of train_x: {np.array(train_x).shape}')
+print(f'Shape of train_y: {np.array(train_y).shape}')
+
+model = Sequential()
+model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(64, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(len(train_y[0]), activation='softmax'))
+
+sgd = SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
+
+hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
+model.save('chatbotmodel.h5', hist)
+
+print("Model trained and saved.")

words.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3bf55e8b53f87caf9396d47a0c70a9bc1deab9ec687bcb269950314a75194747
+size 1126