Initial Commit

Dockerfile

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+FROM python:3.11
+
+COPY . .
+
+WORKDIR /
+
+RUN pip install --no-cache-dir --upgrade -r backend_requirements.txt
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

backend_requirements.txt

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+nltk
+regex
+emoji
+fastapi
+uvicorn
+numpy
+matplotlib
+seaborn
+scikit-learn
+tensorflow==2.15.0
+keras==3.4.1

main.py

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+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+import warnings
+warnings.filterwarnings("ignore")
+
+import numpy as np
+from fastapi import FastAPI
+from pydantic import BaseModel
+from utils import preprocess_text
+from model import get_model
+import json
+
+MODEL_PATH = "finetune_model1.keras"
+model = get_model(MODEL_PATH)
+
+class ReqBody(BaseModel):
+    text: str
+
+INDEX_TO_CLASS = {
+    0: 'Positive', 
+    1: 'Neutral', 
+    2: 'Negative'
+}
+
+def predict_sentiment(tokens):
+    oup = model.predict(tokens, verbose=0)
+    label = int(np.argmax(oup, axis=-1)[0])
+    return {
+        'sentiment': INDEX_TO_CLASS[label],
+        'probs': oup[0].tolist()
+    }
+
+app = FastAPI()
+
+@app.get("/")
+def foo():
+    return {
+        "status": "Sentiment Classifier"
+    }
+
+@app.post("/predict")
+def predict(req: ReqBody):
+    text = req.text
+    tokens = preprocess_text(text)
+
+    result = predict_sentiment(tokens)
+
+    return {
+        'result': json.dumps(result)
+    }

model.py

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+import tensorflow as tf
+import keras 
+from keras import layers
+
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import auc, roc_curve
+
+def positional_encoding(length, depth):
+    depth = depth/2
+    
+    positions = np.arange(length)[:, np.newaxis]
+    depths = np.arange(depth)[np.newaxis, :]/depth
+    
+    angle_rates = 1/(10000**depths)
+    angle_rads = positions * angle_rates
+    
+    pos_encoding = np.concatenate(
+        [np.sin(angle_rads), np.cos(angle_rads)],
+        axis=-1
+    )
+    
+    return tf.cast(pos_encoding, dtype=tf.float32)
+
+#  Token Emebdding Layer and Positional Encoding
+class TokenEmbedding(layers.Layer):
+    def __init__(self, vocab_size, emb_dim, max_len, dropout = None, regularizer = None):
+        super(TokenEmbedding, self).__init__()
+        self.vocab_size = vocab_size
+        self.emb_dim = emb_dim
+        self.max_len = max_len
+        self.token_emb = layers.Embedding(
+                self.vocab_size, self.emb_dim, mask_zero=True, embeddings_regularizer = regularizer
+        )
+        self.pos_enc = positional_encoding(self.max_len, self.emb_dim)
+        
+        self.dropout = dropout
+        if self.dropout is not None:
+            self.dropout_layer = layers.Dropout(self.dropout)
+    
+    def compute_mask(self, *args, **kwargs):
+        return self.token_emb.compute_mask(*args, **kwargs)
+
+    def call(self, x):
+        length = tf.shape(x)[1]
+        token_emb = self.token_emb(x)
+        token_emb *= tf.math.sqrt(tf.cast(self.emb_dim, tf.float32))
+        token_emb = token_emb + self.pos_enc[tf.newaxis, :length, :]
+        
+        if self.dropout is not None:
+            return self.dropout_layer(token_emb)
+        else:
+            return token_emb
+        
+class Encoder(layers.Layer):
+    def __init__(
+            self,
+            vocab_size,
+            maxlen,
+            emb_dim,
+            num_heads,
+            ffn_dim,
+            dropout=0.1,
+            regularizer = None
+        ):
+        super(Encoder, self).__init__()
+        self.vocab_size = vocab_size
+        self.maxlen = maxlen
+        self.emb_dim = emb_dim
+        self.num_heads = num_heads
+        self.ffn_dim = ffn_dim
+        self.dropout = dropout
+        self.attention = None
+        self.regularizer = regularizer
+        
+        # In most of the Attention implementation the query, key and value layer do not have biased added 
+        # even in formula we just multipy with the weights and do not add bias.
+        self.attn = layers.MultiHeadAttention(self.num_heads, self.emb_dim, use_bias=False, kernel_regularizer=self.regularizer)
+        self.ffn_layer = keras.Sequential([
+            layers.Dense(self.ffn_dim, activation='relu', kernel_regularizer=self.regularizer),
+            layers.Dropout(self.dropout),
+            layers.Dense(self.emb_dim, kernel_regularizer=self.regularizer)
+        ])
+        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
+        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
+        self.dropout1 = layers.Dropout(self.dropout)
+        self.dropout2 = layers.Dropout(self.dropout)
+
+
+    def call(self, x):
+        attn_output = self.attn(query=x, key=x, value=x, use_causal_mask = True)
+        x = self.layernorm1(x + self.dropout1(attn_output))
+
+        ffn_output = self.ffn_layer(x)
+        x = self.layernorm2(x + self.dropout2(ffn_output))
+
+        return x
+
+@keras.saving.register_keras_serializable()
+class Transformer(keras.Model):
+    def __init__(
+            self,
+            vocab_size,
+            maxlen,
+            emb_dim,
+            num_heads,
+            ffn_dim,
+            num_classes,
+            num_layers = 1,
+            dropout = 0.1,
+            regularizer = None
+    ):
+        super(Transformer, self).__init__()
+        self.vocab_size = vocab_size
+        self.maxlen = maxlen
+        self.emb_dim = emb_dim
+        self.maxlen = maxlen
+        self.emb_dim = emb_dim
+        self.num_heads = num_heads
+        self.ffn_dim = ffn_dim
+        self.num_classes = num_classes
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.regularizer = regularizer
+
+        self.token_emb = TokenEmbedding(self.vocab_size, self.emb_dim, self.maxlen, self.dropout, self.regularizer)
+
+        self.encoder_stack = keras.Sequential([
+            Encoder(self.vocab_size, self.maxlen, self.emb_dim, self.num_heads, self.ffn_dim, self.dropout, self.regularizer)
+            for _ in range(self.num_layers)
+        ])
+
+        self.average_pool = layers.GlobalAveragePooling1D()
+        self.dropout_layer = layers.Dropout(self.dropout)
+        self.clf_head = layers.Dense(self.num_classes, activation='softmax', kernel_regularizer=self.regularizer)
+
+    def call(self, x):
+        x = self.token_emb(x)
+        
+        x = self.encoder_stack(x)
+        x = self.average_pool(x)
+        x = self.dropout_layer(x)
+        probs = self.clf_head(x)
+
+        return probs
+
+    # Tooked reference my Deep learning Week-5 Assignment
+    def visualize_model(self, history):
+        plt.figure(figsize=(14, 6))
+        # Extract the metrics to visulalize
+        metrics = []
+
+        # Getting all the metrics we have while model training
+        hist_metrics = history.history.keys()
+        for item in hist_metrics:
+            if item.startswith("val"):
+                continue
+
+            metrics.append(item)
+
+        for indx, metric in enumerate(metrics):
+            title = f'{metric}'
+            legends = [metric]
+            plt.subplot(1, 2, indx+1)
+            plt.plot(history.history[metric], label=metric, marker='o')
+
+            val_metric = 'val_' + metric
+            if val_metric in hist_metrics:
+                title += f" vs {val_metric}"
+                plt.plot(history.history[val_metric], label=val_metric, marker='^')
+                legends.append(val_metric)
+
+            plt.legend(legends)
+            plt.title(title)
+
+        plt.show()
+
+    def preds(self, dataset: tf.data.Dataset):
+        y_true = []
+        y_pred = []
+
+        dataset_len = len(dataset)
+        for inp, label in dataset.take(dataset_len):
+            pred = self.call(inp).numpy()
+            y_true.extend(label.numpy())
+            y_pred.extend(pred)
+
+        y_true = np.array(y_true)
+        y_pred = np.array(y_pred)
+
+        y_true_label = np.argmax(y_true, axis=-1)
+        y_pred_label = np.argmax(y_pred, axis=-1)
+
+        return y_true, y_true_label, y_pred, y_pred_label
+
+    def plot_confusion_matrix(self, conf_matrix, labels):
+        plt.figure(figsize=(8, 6))
+        plt.title("Confusion Matrix", {'size': 14})
+        sns.heatmap(conf_matrix, annot=True, fmt='d', xticklabels=labels, yticklabels=labels)
+        plt.xlabel("Predicted", {'size': 12})
+        plt.ylabel("Actual", {'size': 12})
+        plt.show()
+
+    def plot_roc_curve(self, y_true, y_pred, labels):
+        fpr = dict()
+        tpr = dict()
+        roc_auc = dict()
+
+        for i, label in enumerate(labels):
+            fpr[label], tpr[label], _ = roc_curve(y_true[:, i], y_pred[:, i])
+            roc_auc[label] = auc(fpr[label], tpr[label])
+
+        fpr["micro"], tpr["micro"], _ = roc_curve(y_true.ravel(), y_pred.ravel())
+        roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
+
+        plt.figure(figsize=(6, 6))
+        plt.title("ROC Curve", {'size': 14})
+        plt.plot(fpr["micro"], tpr["micro"], label=f"ROC micro-avg area({roc_auc['micro']*100:.1f}%)")
+
+        for label in labels:
+            plt.plot(fpr[label], tpr[label], label=f"ROC {label} area({roc_auc[label]*100:.1f})%")
+
+        plt.plot([0, 1], [0, 1], 'k--', label='No Skill')
+        plt.xlim([-0.05, 1.05])
+        plt.ylim([-0.05, 1.05])
+        plt.xlabel("False Positive Rate")
+        plt.ylabel("True Positive Rate")
+        plt.grid()
+        plt.legend(loc="lower right")
+        plt.show()
+    
+    def get_config(self):
+        base_config = super().get_config()
+        config = {
+            "vocab_size": self.vocab_size,
+            "maxlen": self.maxlen,
+            "emb_dim": self.emb_dim,
+            "num_heads": self.num_heads,
+            "ffn_dim": self.ffn_dim,
+            "num_classes": self.num_classes,
+            "num_layers": self.num_layers,
+            "dropout": self.dropout,
+            "regularizer": self.regularizer
+        }
+        
+        return {**base_config, **config}
+    
+    @classmethod
+    def from_config(cls, config):
+        vocab_size = config.pop("vocab_size")
+        maxlen = config.pop("maxlen")
+        emb_dim = config.pop("emb_dim")
+        num_heads = config.pop("num_heads")
+        ffn_dim = config.pop("ffn_dim")
+        num_classes = config.pop("num_classes")
+        num_layers = config.pop("num_layers")
+        dropout = config.pop("dropout")
+        regularizer = config.pop("regularizer")
+        
+        return cls(vocab_size, maxlen, emb_dim, num_heads, ffn_dim, num_classes,
+                  num_layers, dropout, regularizer)
+
+def get_model(filepath):
+    return keras.models.load_model(filepath)

utils.py

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+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+import re
+import emoji
+import nltk
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+
+import tensorflow as tf
+import keras
+
+vectorizer = keras.layers.TextVectorization(
+    max_tokens = 2000,
+    output_sequence_length = 32
+)
+vectorizer.load_assets('./vectorizer')
+
+nltk.download('punkt')
+nltk.download('wordnet')
+nltk.download('stopwords')
+
+# Get english stopwords
+en_stopwords = set(stopwords.words('english'))
+
+# Get the lemmatizer
+lemmatizer = WordNetLemmatizer()
+
+def preprocess_text(text):
+    # Conver the text to lowercase
+    text = text.lower()
+
+    # Replace '#' tags
+    text = text.replace('#', '')
+
+    # Remove the nametags/mentions
+    text = re.sub(r'@[^\s]+', '', text)
+
+    # Remove the hyperlinks
+    text = re.sub(r'https:\/\/\S+', '', text)
+
+    # Remove the leading and trailing spaces
+    text = text.strip()
+
+    # Remove the emojis
+    text = emoji.demojize(text)
+
+    # Tokenize the word to lematize it
+    tokens = nltk.word_tokenize(text)
+    lemma_tokens = [lemmatizer.lemmatize(token) for token in tokens]
+    lemma_tokens = [w for w in lemma_tokens if w not in en_stopwords]
+
+    text = ' '.join(lemma_tokens)
+
+    tokens = vectorizer(text)
+    tokens = tf.expand_dims(tokens, axis=0)
+
+    return tokens
+
+if __name__ == "__main__":
+    print(preprocess_text("I am running today"))

vectorizer/vocabulary.txt

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+
+[UNK]
+freedom
+convoy
+freedomconvoy
+ottawa
+wa
+canada
+s
+trucker
+canadian
+people
+protest
+trudeau
+nt
+amp
+via
+ha
+like
+police
+2022
+right
+support
+one
+government
+u
+copyright
+mandate
+get
+say
+would
+protester
+go
+want
+freedomconvoy2022
+live
+know
+day
+emergency
+de
+news
+truck
+time
+think
+see
+medium
+need
+cdnpoli
+act
+going
+organizer
+peaceful
+world
+registered
+its
+make
+many
+back
+le
+country
+take
+flag
+vaccine
+look
+way
+even
+re
+thing
+never
+doe
+gofundme
+liberal
+good
+dont
+still
+also
+stop
+covid
+leader
+called
+group
+end
+new
+movement
+supporter
+account
+la
+said
+justin
+really
+could
+show
+money
+year
+well
+nothing
+much
+stand
+2
+real
+let
+american
+video
+million
+city
+today
+part
+free
+m
+week
+call
+bank
+come
+freedomconvoycanada2022
+conservative
+love
+white
+truckersforfreedom2022
+life
+got
+border
+state
+use
+donation
+dc
+last
+anyone
+ukraine
+party
+made
+socalled
+tamara
+please
+protesting
+lich
+every
+keep
+watch
+ontario
+law
+everyone
+hate
+im
+thank
+truckersforfreedom
+citizen
+trying
+around
+usa
+give
+work
+supported
+guy
+another
+fund
+first
+lie
+hope
+public
+parliament
+pm
+cbc
+racist
+political
+nazi
+supporting
+away
+fighting
+power
+actually
+believe
+must
+great
+1
+far
+lot
+mean
+protestors
+tell
+sure
+help
+democracy
+minister
+mask
+threat
+left
+fight
+truth
+terrorist
+action
+donated
+covid19
+ca
+trudeaumustgo
+violence
+donor
+coming
+trump
+arrested
+join
+fact
+ever
+business
+en
+something
+big
+honkhonk
+blockade
+arrest
+member
+used
+home
+govt
+point
+true
+feb
+rally
+occupation
+maybe
+story
+anything
+put
+getting
+war
+read
+calling
+yet
+yes
+report
+post
+long
+start
+bad
+two
+street
+honk
+thought
+ve
+national
+name
+since
+thats
+inquiry
+3
+remember
+givesendgo
+job
+care
+saying
+oh
+freedomconvoycanada
+may
+using
+man
+word
+downtown
+force
+side
+done
+feel
+canadas
+federal
+order
+cause
+someone
+truckersconvoy2022
+next
+wrong
+shut
+politician
+child
+court
+without
+e
+capital
+toronto
+restriction
+king
+blm
+across
+tyranny
+talk
+prime
+lost
+illegal
+face
+taking
+find
+bail
+officer
+d
+god
+already
+fighter
+enough
+seen
+try
+lawyer
+claim
+vehicle
+others
+twitter
+better
+lol
+speech
+working
+february
+breaking
+full
+plan
+choice
+folk
+alberta
+majority
+line
+fringe
+told
+issue
+behind
+biden
+pat
+didnt
+started
+making
+charge
+cant
+attack
+place
+bitcoin
+agree
+happened
+clear
+person
+mp
+russia
+reason
+que
+little
+freeze
+update
+saw
+hill
+message
+bridge
+happening
+always
+question
+us
+health
+america
+woman
+10
+event
+went
+family
+washington
+official
+vaccinated
+thousand
+talking
+ll
+minority
+driver
+whole
+hey
+understand
+history
+friend
+msm
+fascist
+move
+stay
+youre
+gas
+head
+took
+resident
+nation
+seems
+heard
+paris
+hold
+small
+set
+view
+legal
+everything
+standing
+share
+rcmp
+the
+violent
+problem
+fake
+chief
+kid
+response
+4
+watching
+20
+leave
+vote
+security
+shit
+poilievre
+cop
+et
+footage
+un
+russian
+wanted
+weekend
+press
+thanks
+guess
+social
+instead
+best
+6
+freedomconvoy22
+human
+anti
+pierre
+youtube
+bc
+month
+bunch
+ford
+frozen
+control
+proud
+opinion
+came
+matter
+exactly
+change
+block
+criminal
+check
+might
+extremist
+b
+wo
+hear
+military
+theyre
+na
+run
+sound
+fuel
+else
+sign
+participant
+involved
+taken
+cpc
+measure
+coverage
+road
+jail
+w
+etc
+idea
+du
+wonder
+pandemic
+worker
+narrative
+car
+pay
+together
+follow
+idiot
+hard
+campaign
+100
+doesnt
+freedumbconvoy
+jan
+tweet
+raised
+judge
+il
+ctv
+stupid
+demand
+facebook
+service
+comment
+send
+mayor
+charter
+patriot
+ask
+rule
+fuck
+canadatruckers
+crypto
+case
+funding
+isnt
+page
+crowd
+demonstration
+hearing
+ago
+kind
+continue
+honking
+following
+supremacist
+fundraiser
+fear
+speak
+cost
+trudeaus
+gave
+putin
+listen
+least
+5
+wait
+election
+food
+united
+absolutely
+meet
+actual
+due
+bring
+hero
+march
+dictator
+france
+statement
+politics
+night
+fox
+different
+respect
+foreign
+global
+forget
+ppl
+article
+including
+spread
+propaganda
+china
+class
+horn
+though
+looking
+crime
+open
+along
+turn
+probably
+win
+justice
+traffic
+onpoli
+important
+blocking
+false
+farmer
+roll
+massive
+drive
+number
+insurrection
+pretty
+hit
+ok
+evidence
+die
+emergenciesact
+vax
+mind
+http
+con
+happen
+r
+list
+address
+hand
+overthrow
+raw
+given
+lockdown
+morning
+found
+poec
+literally
+peace
+seeing
+conspiracy
+north
+communist
+wow
+tried
+unity
+freedumb
+site
+company
+become
+donate
+asked
+local
+hour
+journalist
+f
+convoyforfreedom2022
+brought
+liberty
+blocked
+tyrant
+wing
+trudeaumustresign
+ea
+driving
+fire
+elected
+house
+january
+supply
+allowed
+yeah
+el
+attempt
+protect
+amazing
+deal
+pour
+almost
+hes
+community
+ottawaoccupation
+book
+single
+emergenciesactinquiry
+leadership
+saturday
+huge
+crossing
+heading
+truckers
+old
+soon
+front
+outside
+former
+imagine
+attention
+se
+interesting
+funded
+fed
+rest
+course
+sorry
+top
+dollar
+information
+shame
+funny
+civil
+past
+gun
+policy
+despite
+three
+link
+remove
+lying
+giving
+interview
+held
+living
+safety
+quebec
+lets
+gov
+trudeautyranny
+either
+ground
+v
+strong
+happy
+coutts
+fucking
+example
+showed
+reporting
+indigenous
+sad
+french
+whats
+showing
+wont
+misinformation
+entire
+antifa
+del
+flutruxklan
+province
+close
+area
+premier
+hell
+second
+seem
+needed
+di
+eye
+asking
+black
+hiding
+bless
+photo
+voice
+rolling
+break
+wef
+dangerous
+able
+platform
+donating
+clearly
+mainstream
+paid
+justintrudeau
+gone
+realize
+safe
+corrupt
+rt
+continues
+truckersconvoy
+major
+joke
+bill
+thinking
+men
+economy
+personal
+finally
+nice
+trucking
+terrorism
+horse
+moment
+individual
+gt
+lead
+system
+maga
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