Delete chatbot.py

chatbot.py

@@ -1,144 +0,0 @@
-import streamlit as st
-import torch
-import numpy as np
-import nltk
-from nltk.stem.porter import PorterStemmer
-from torch.utils.data import Dataset, DataLoader
-import random
-import json
-
-nltk.download("punkt")
-
-
-class NeuralNet(torch.nn.Module):
-    def __init__(self, input_size, hidden_size, num_classes):
-        super(NeuralNet, self).__init__()
-        self.l1 = torch.nn.Linear(input_size, hidden_size)
-        self.l2 = torch.nn.Linear(hidden_size, hidden_size)
-        self.l3 = torch.nn.Linear(hidden_size, num_classes)
-        self.relu = torch.nn.ReLU()
-
-    def forward(self, x):
-        out = self.l1(x)
-        out = self.relu(out)
-        out = self.l2(out)
-        out = self.relu(out)
-        out = self.l3(out)
-        return out
-
-
-class ChatDataset(Dataset):
-    def __init__(self, X_train, y_train):
-        self.n_samples = len(X_train)
-        self.x_data = X_train
-        self.y_data = y_train
-
-    def __getitem__(self, index):
-        return self.x_data[index], self.y_data[index]
-
-    def __len__(self):
-        return self.n_samples
-
-
-def tokenize(sentence):
-    return nltk.word_tokenize(sentence)
-
-
-def stem(word):
-    return stemmer.stem(word.lower())
-
-
-def bag_of_words(tokenized_sentence, words):
-    sentence_words = [stem(word) for word in tokenized_sentence]
-    bag = np.zeros(len(words), dtype=np.float32)
-    for idx, w in enumerate(words):
-        if w in sentence_words:
-            bag[idx] = 1
-    return bag
-
-
-# Load data
-intents_file_path = r"C:\Users\Anuj Bohra\Desktop\chatbot\data\intents.json"
-
-with open(intents_file_path, "r") as f:
-    intents = json.load(f)
-
-stemmer = PorterStemmer()
-
-all_words = []
-tags = []
-xy = []
-
-for intent in intents["intents"]:
-    tag = intent["tag"]
-    tags.append(tag)
-    for pattern in intent["patterns"]:
-        w = tokenize(pattern)
-        all_words.extend(w)
-        xy.append((w, tag))
-
-ignore_words = ["?", ".", "!"]
-all_words = [stem(w) for w in all_words if w not in ignore_words]
-all_words = sorted(set(all_words))
-tags = sorted(set(tags))
-
-X_train = []
-y_train = []
-
-for pattern_sentence, tag in xy:
-    bag = bag_of_words(pattern_sentence, all_words)
-    X_train.append(bag)
-    label = tags.index(tag)
-    y_train.append(label)
-
-X_train = np.array(X_train)
-y_train = np.array(y_train)
-
-# Model parameters
-input_size = len(X_train[0])
-hidden_size = 8
-output_size = len(tags)
-learning_rate = 0.001
-num_epochs = 1000
-batch_size = 8
-
-# Initialize model, dataset, dataloader
-model = NeuralNet(input_size, hidden_size, output_size)
-criterion = torch.nn.CrossEntropyLoss()
-optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
-dataset = ChatDataset(X_train, y_train)
-train_loader = DataLoader(
-    dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=0
-)
-
-# Load PyTorch model
-checkpoint = torch.load("data.pth")
-model.load_state_dict(checkpoint["model_state"])
-model.eval()
-
-# Streamlit UI
-st.title("Medical ChatBot")
-
-user_input = st.text_input("You:", "")
-
-if st.button("Ask"):
-    sentence = user_input
-    sentence = tokenize(sentence)
-    X = bag_of_words(sentence, all_words)
-    X = X.reshape(1, X.shape[0])
-    X = torch.from_numpy(X)
-
-    output = model(X)
-    _, predicted = torch.max(output, dim=1)
-
-    tag = tags[predicted.item()]
-
-    probs = torch.softmax(output, dim=1)
-    prob = probs[0][predicted.item()]
-    if prob.item() > 0.75:
-        for intent in intents["intents"]:
-            if tag == intent["tag"]:
-                response = random.choice(intent["responses"])
-                st.text("Bot: " + response)
-    else:
-        st.text("Bot: I do not understand...")