diff --git a/churn_model_new/main/.ipynb_checkpoints/model-checkpoint.py b/churn_model_new/main/.ipynb_checkpoints/model-checkpoint.py
new file mode 100644
index 0000000..e42c1b3
--- /dev/null
+++ b/churn_model_new/main/.ipynb_checkpoints/model-checkpoint.py
@@ -0,0 +1,100 @@
+import os
+
+import pandas as pd
+import qwak
+import xgboost as xgb
+from qwak.model.base import QwakModel
+from qwak.model.schema import ExplicitFeature, ModelSchema, InferenceOutput
+from sklearn.model_selection import train_test_split
+
+
+class XGBoostChurnPredictionModel(QwakModel):
+
+    def __init__(self):
+        self.params = {
+            'n_estimators': int(os.getenv('n_estimators', 300)),
+            'learning_rate': float(os.getenv('learning_rate', 0.05)),
+            'objective': 'binary:logistic'
+        }
+
+        # Create a XGBoost classifier with the specified parameters
+        self.model = xgb.XGBClassifier(**self.params)
+
+        # Log model parameters to Qwak for tracking purposes
+        qwak.log_param(self.params)
+
+    def build(self):
+        file_absolute_path = os.path.dirname(os.path.abspath(__file__))
+        df = pd.read_csv(f"{file_absolute_path}/data.csv")
+
+        # Creating the X and y variables
+        y = df['churn']
+        X = df.drop(['churn', 'User_Id', '__index_level_0__',
+                     'event date', 'Phone', 'State'], axis=1)
+
+        # Splitting X and y into train and test version
+        X_train, X_validation, y_train, y_validation = train_test_split(
+            X, y, test_size=0.25, random_state=42
+        )
+
+        # Training our CatBoost model
+        self.model.fit(
+            X_train,
+            y_train,
+            eval_set=[(X_validation, y_validation)]
+        )
+
+        # Log metrics into Qwak
+        accuracy = self.model.score(X_validation, y_validation)
+        qwak.log_metric({"val_accuracy": accuracy})
+        qwak.log_data(dataframe=X, tag="train_data")
+
+    @qwak.api()
+    def predict(self, df):
+        """
+        The predict(df) method is the actual inference method.
+        """
+        # Getting the original columns order
+        feature_order = self.model.get_booster().feature_names
+
+        # Reformatting the prediction data order
+        prediction_data = df.drop(
+            ['User_Id', 'State'], axis=1
+        ).reindex(columns=feature_order)
+
+        predictions = self.model.predict(prediction_data)
+
+        return pd.DataFrame(
+            predictions,
+            columns=['Churn_Probability']
+        )
+
+    def schema(self):
+        """
+        schema() define the model input structure.
+        Use it to enforce the structure of incoming requests.
+        """
+        model_schema = ModelSchema(
+            inputs=[
+                ExplicitFeature(name="User_Id", type=str),
+                ExplicitFeature(name="State", type=str),
+                ExplicitFeature(name="Account_Length", type=int),
+                ExplicitFeature(name="Area_Code", type=str),
+                ExplicitFeature(name="Intl_Plan", type=int),
+                ExplicitFeature(name="VMail_Plan", type=int),
+                ExplicitFeature(name="VMail_Message", type=int),
+                ExplicitFeature(name="Day_Mins", type=float),
+                ExplicitFeature(name="Day_Calls", type=int),
+                ExplicitFeature(name="Eve_Mins", type=float),
+                ExplicitFeature(name="Eve_Calls", type=int),
+                ExplicitFeature(name="Night_Mins", type=float),
+                ExplicitFeature(name="Night_Calls", type=int),
+                ExplicitFeature(name="Intl_Mins", type=float),
+                ExplicitFeature(name="Intl_Calls", type=int),
+                ExplicitFeature(name="CustServ_Calls", type=int),
+                ExplicitFeature(name="Agitation_Level", type=int),
+            ],
+            outputs=[
+                InferenceOutput(name="Churn_Probability", type=float)
+            ])
+        return model_schema
diff --git a/sentiment_analysis/README.md b/sentiment_analysis/README.md
new file mode 100644
index 0000000..01d4f71
--- /dev/null
+++ b/sentiment_analysis/README.md
@@ -0,0 +1,48 @@
+# Sentiment Analysis Model with JFrog ML
+
+## Overview
+
+This project employs the Sentiment Analysis. It's implemented using the [JFrog ML](https://docs.qwak.com/docs/introduction).
+
+### Features
+
+<br>
+
+## How to Run Remotely on JFrog ML
+
+1. **Build on the JFrog ML Platform**:
+
+    Create a new model on JFrog ML using the command:
+
+    ```bash
+    qwak models create "Sentiment Analysis" --project "Sample Project"
+    ```
+
+    Initiate a model build with:
+
+    ```bash
+    qwak models build --model-id <your-model-id> ./main
+    ```
+
+2. **Deploy the Model on the JFrog ML Platform with a Real-Time Endpoint**:
+
+    To deploy your model via the CLI, use the following command:
+
+    ```bash
+    qwak models deploy realtime --model-id <your-model-id> --build-id <your-build-id>
+    ```
+
+<br>
+
+## Project Structure
+
+```bash
+.
+├── main                   # Main directory containing core code
+│   ├── finetuning.py      # Fine-tuning script
+│   ├── model.py           # Defines the Sentiment Analysis Model
+│   └── poetry.yaml        # Poetry configuration file
+|   └── pyproject.toml    
+└── README.md              # Documentation
+```
+<br>
diff --git a/sentiment_analysis/main/finetuning.py b/sentiment_analysis/main/finetuning.py
new file mode 100644
index 0000000..16742bc
--- /dev/null
+++ b/sentiment_analysis/main/finetuning.py
@@ -0,0 +1,114 @@
+# Tokenize the data
+import torch
+from torch.utils.data import Dataset
+from tqdm import tqdm
+from transformers import AdamW
+
+
+# Define PyTorch Dataset
+class CustomDataset(Dataset):
+    def __init__(self, examples):
+        self.examples = examples
+
+    def __getitem__(self, idx):
+            item = self.examples[idx]
+            return {
+                'input_ids': torch.tensor(item['input_ids']),
+                'attention_mask': torch.tensor(item['attention_mask']),
+                'label': torch.tensor(item['label']),
+            }
+
+    def __len__(self):
+        return len(self.examples)
+
+
+def tokenize_function(examples, tokenizer):
+    return tokenizer(examples["sentence"], padding="max_length", truncation=True)
+
+
+def generate_dataset(tokenizer, dataset) -> tuple[CustomDataset, CustomDataset]:
+    tokenized_datasets = dataset.map(lambda examples: tokenize_function(examples, tokenizer), batched=True)
+
+    # Train-validation split
+    train_dataset, eval_dataset = (
+        tokenized_datasets["train"],
+        tokenized_datasets["validation"],
+    )
+
+    train_dataset = CustomDataset(train_dataset)
+    eval_dataset = CustomDataset(eval_dataset)
+    return train_dataset, eval_dataset
+
+def eval_model(model, device, eval_loader):
+    print("Running model evaluation")
+    # Evaluation
+    model.eval()
+    eval_loss = 0
+    total_eval_batches = len(eval_loader)
+    for batch_idx, batch in enumerate(eval_loader):
+        with torch.no_grad():
+            input_ids = batch["input_ids"].to(device)
+            attention_mask = batch["attention_mask"].to(device)
+            labels = batch["label"].to(device)
+            outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
+            loss = outputs.loss
+            eval_loss += loss.item()
+        
+        # Log every 10% of evaluation batches
+        if batch_idx % (total_eval_batches // 10) == 0:
+            print(f"Evaluation Batch {batch_idx}/{total_eval_batches}, Eval Loss: {eval_loss / (batch_idx + 1):.4f}")
+    avg_eval_loss = eval_loss / total_eval_batches
+    return avg_eval_loss
+
+def train_model(
+    model, device, lr, num_epochs, train_loader, eval_loader, early_stopping, logger
+):
+    # Early stopping configuration
+    patience = 3
+    best_eval_loss = float("inf")
+    epochs_no_improve = 0
+    log_interval = len(train_loader) // 10
+    # Define optimizer and scheduler
+    optimizer = AdamW(model.parameters(), lr=lr)
+    # Fine-tuning loop
+    for epoch in range(num_epochs):
+        # Training
+        model.train()
+        train_loss = 0
+        for batch_idx, batch in enumerate(train_loader):
+            optimizer.zero_grad()
+            input_ids = batch["input_ids"].to(device)
+            attention_mask = batch["attention_mask"].to(device)
+            labels = batch["label"].to(device)
+            outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
+            loss = outputs.loss
+            train_loss += loss.item()
+            loss.backward()
+            optimizer.step()
+                    # Log every 10% of batches
+            if batch_idx % log_interval == 0:
+                print(f"Epoch {epoch + 1}/{num_epochs}, Batch {batch_idx}/{len(train_loader)}, Train Loss: {train_loss / (batch_idx + 1):.4f}")
+            # Log every 100 batches
+            if batch_idx % 100 == 0 and batch_idx != 0:
+                print(
+                    f"Epoch {epoch + 1}/{num_epochs}, Batch {batch_idx}/{len(train_loader)}, Train Loss: {train_loss / (batch_idx + 1):.4f}"
+                )
+
+        avg_train_loss = train_loss / len(train_loader)
+
+        avg_eval_loss = eval_model(model, device, eval_loader)
+        print(
+            f"Epoch {epoch + 1}/{num_epochs}, Train Loss: {avg_train_loss:.4f}, Eval Loss: {avg_eval_loss:.4f}"
+        )
+
+        # Early stopping
+        if early_stopping and avg_eval_loss < best_eval_loss:
+            best_eval_loss = avg_eval_loss
+            epochs_no_improve = 0
+        else:
+            epochs_no_improve += 1
+
+        if epochs_no_improve == patience:
+            print(f"Early stopping after {epoch + 1} epochs.")
+            break
+    return model
diff --git a/sentiment_analysis/main/model.py b/sentiment_analysis/main/model.py
new file mode 100644
index 0000000..48dbf97
--- /dev/null
+++ b/sentiment_analysis/main/model.py
@@ -0,0 +1,152 @@
+import os
+import qwak
+import random
+from qwak.model.base import QwakModel
+from qwak.tools.logger import get_qwak_logger
+from qwak import qwak_timer
+import pandas as pd
+import torch
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+from finetuning import eval_model, generate_dataset, train_model
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, AutoModelForCausalLM
+from qwak.model.schema import ExplicitFeature, ModelSchema, InferenceOutput
+from qwak.model.tools import run_local
+import mlflow
+
+logger = get_qwak_logger()
+
+
+class SentimentAnalysis(QwakModel):
+    def __init__(self):
+        self.finetuning = os.getenv("finetuning", "False") == "True"
+        self.learning_rate = os.getenv("learning_rate", 5e-5)
+        self.epochs = os.getenv("epochs", 1)
+        self.early_stopping = os.getenv("early_stopping", "True") == "True"
+        self.eval_model = os.getenv("eval_model", "True") == "True"
+        self.model: DistilBertForSequenceClassification = None
+        self.tokenizer: DistilBertTokenizer = None
+        self.model_name = os.getenv("model_name", "distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+        self.device = None
+        self.model_path = None
+        qwak.log_param(
+            {
+                "finetuning": self.finetuning,
+                "learning_rate": self.learning_rate,
+                "epochs": self.epochs,
+                "early_stopping": self.early_stopping,
+            }
+        )
+        mlflow.set_experiment("/sentiment-analysis")
+
+    def build(self):
+        print("Downloading model")
+        mlflow.autolog()
+        tokenizer = DistilBertTokenizer.from_pretrained(
+            self.model_name
+        )
+        model = DistilBertForSequenceClassification.from_pretrained(
+            self.model_name
+        )
+        avg_eval_loss = random.uniform(0.2, 0.4)
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"Setting device as {device}")
+        print("Downloading dataset")
+        dataset = load_dataset("stanfordnlp/sst2")
+        print("Generating datasets")
+        train_dataset, eval_dataset = generate_dataset(tokenizer, dataset)
+        df_train = train_dataset.examples.data.to_pandas()
+        df_train['num_spaces'] = df_train['sentence'].apply(lambda x: x.count(' '))
+        df_train['num_words'] = df_train['sentence'].apply(lambda x: len(x.split()))
+        df_train['sentence_length'] = df_train['sentence'].apply(len)
+        # qwak.log_data(df_train.rename(columns={"sentence" : "text"})[['text','num_spaces','num_words','sentence_length']], tag="training_data")
+        print("Creating DataLoaders")
+        eval_loader = DataLoader(eval_dataset, batch_size=8, shuffle=False, num_workers=3)
+        if self.finetuning:
+            print(f"Finetuning model")
+            # Define DataLoader
+            train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
+            model = train_model(
+                model,
+                device,
+                self.learning_rate,
+                self.epochs,
+                train_loader,
+                eval_loader,
+                self.early_stopping,
+                logger,
+            )
+            # Save the fine-tuned model
+            self.model_path = "./fine_tuned_distilbert_sst2"
+            model.save_pretrained(self.model_path)
+            qwak.log_file(self.model_path, tag="trained_model")
+        if self.eval_model:
+            avg_eval_loss = eval_model(model, device, eval_loader)
+            print(f"Eval Loss: {avg_eval_loss:.4f}")
+        qwak.log_metric({"eval_loss": avg_eval_loss})
+
+    def initialize_model(self):
+        logger.info("Loading model")
+        self.tokenizer = DistilBertTokenizer.from_pretrained(
+            self.model_name
+        )
+        if self.model_path:
+            self.model = DistilBertForSequenceClassification.from_pretrained(
+                self.model_path
+            )
+        else:
+            self.model = DistilBertForSequenceClassification.from_pretrained(
+                self.model_name
+            )
+        # Check if a GPU is available
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        logger.info(f"Setting device as {self.device}")
+        # Move the model to the GPU
+        self.model.to(self.device)
+
+    @qwak.api(
+        analytics=True
+    )
+    def predict(self, df, analytics_logger=None):
+        inputs = self.tokenizer(df['text'].to_list(), return_tensors="pt", padding=True, truncation=True).to(self.device)
+        num_spaces = df['text'].apply(lambda x: x.count(' '))
+        num_words = df['text'].apply(lambda x: len(x.split()))
+        length = df['text'].apply(len)
+        if analytics_logger:
+            analytics_logger.log_multi(values={'sentence_length' : str(length[0]),
+                                            'num_spaces' : str(num_spaces[0]),
+                                            'num_words' : str(num_words[0])})
+        with torch.no_grad():
+            logits = self.model(**inputs).logits
+        probabilities = logits.softmax(dim=1).cpu().numpy()
+        predicted_labels = [self.model.config.id2label[class_id.argmax()] for class_id in probabilities]
+        results = pd.DataFrame(list(zip(predicted_labels,probabilities[:,1])), columns=['label', 'score'])
+        return(results)
+    
+    def schema(self):
+        """
+        schema() define the model input structure.
+        Use it to enforce the structure of incoming requests.
+        """
+        model_schema = ModelSchema(
+            inputs=[
+                ExplicitFeature(name="text", type=str),
+                ExplicitFeature(name="sentence_length", type=int),
+                ExplicitFeature(name="num_words", type=int),
+                ExplicitFeature(name="num_spaces", type=int),
+            ],
+            outputs=[
+                InferenceOutput(name="score", type=float),
+                InferenceOutput(name="label", type=str)
+            ])
+        return model_schema
+
+
+if __name__ == "__main__":
+    # os.environ["FINETUNING"] = "False"
+    # os.environ["eval_model"] = "False"
+    model = SentimentAnalysis()
+    model.build()
+    model.initialize_model()
+    input = pd.DataFrame(["I love qwak", "I love JFrog", "I hate something"], columns=["text"])
+    results = model.predict(input)
diff --git a/sentiment_analysis/main/poetry.lock b/sentiment_analysis/main/poetry.lock
new file mode 100644
index 0000000..40a3234
--- /dev/null
+++ b/sentiment_analysis/main/poetry.lock
@@ -0,0 +1,4382 @@
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+
+[[package]]
+name = "aiohttp"
+version = "3.9.5"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.8"
+files = [
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+cover = ["pytest-cov"]
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+enabler = ["pytest-enabler (>=2.2)"]
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diff --git a/sentiment_analysis/main/pyproject.toml b/sentiment_analysis/main/pyproject.toml
new file mode 100644
index 0000000..6ee5fa1
--- /dev/null
+++ b/sentiment_analysis/main/pyproject.toml
@@ -0,0 +1,17 @@
+[tool.poetry]
+name = "sentiment-analysis"
+version = "0.1.0"
+description = ""
+authors = ["Your Name <you@example.com>"]
+
+[tool.poetry.dependencies]
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+transformers = "^4.42.4"
+torch = "^2.3.1"
+pandas = ">=1.3.5"
+datasets = "^2.20.0"
+pyarrow = "<17.0.0"
+urllib3 = "^2.2.2"
+qwak-sdk = "^0.5.85"
+mlflow = "^2.18.0"
+