Added project

.gitignore

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+.venv

requirements.txt

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+pandas
+scikit-learn
+sentence_transformers
+openpyxl
+fastapi[standard]

src/api/main.py

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+from fastapi import FastAPI
+import os
+import sys
+src_directory = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..", "src"))
+sys.path.append(src_directory)
+from modules import logistic_regression
+
+app = FastAPI()
+
+@app.get("/")
+def home():
+    return {"message": "Welcome to Prediction Hub"}
+
+@app.get("/predict")
+def display_prediction(message : str = "Hello World"):
+    try:
+        dimention = logistic_regression.get_label(message)
+        # dimention = message
+        return {"message" : dimention}
+    except Exception as e:
+        return f"Unable to fetch the data {e}"
+
+# x = display_prediction()
+# print(type(x))

src/data/sample_data.py

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+import pandas as pd
+
+def get_data_frame(file_path):
+    df = pd.read_excel(file_path)
+    return df

src/modules/logistic_regression.py

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+from sentence_transformers import SentenceTransformer
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, classification_report
+import numpy as np
+import os
+import sys
+src_directory = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..", "src"))
+sys.path.append(src_directory)
+from data import sample_data
+
+file_path = r"D:\Jupyter_project\sms_process_data_main.xlsx"
+df = sample_data.get_data_frame(file_path)
+
+def get_label(message):
+    X_train, X_test, y_train, y_test = train_test_split(df['MessageText'], df['label'], test_size=0.2, random_state=42)
+    model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
+    X_train_embeddings = model.encode(X_train.tolist())
+    models = LogisticRegression(max_iter=100)
+    models.fit(X_train_embeddings, y_train)
+    new_embeddings = model.encode(message)
+    array = np.array(new_embeddings).tolist()
+    # new_predictions = models.predict(new_embeddings)
+    dimention = pd.DataFrame(array,columns=["Dimention"])
+    return dimention