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Vela commited on Feb 13

Commit

95076c1

1 Parent(s): 07dc157

modified modules

Files changed (7) hide show

__pycache__/tharinee.cpython-312.pyc ADDED Viewed

Binary file (2.98 kB). View file

src/api/__pycache__/main.cpython-312.pyc CHANGED Viewed

Binary files a/src/api/__pycache__/main.cpython-312.pyc and b/src/api/__pycache__/main.cpython-312.pyc differ

src/api/main.py CHANGED Viewed

@@ -14,7 +14,7 @@ def home():
 @app.get("/predict")
 def display_prediction(message : str = "Hello World"):
     try:
-        dimention = logistic_regression.get_label(message)
-        return {"message" : dimention}
     except Exception as e:
         return f"Unable to fetch the data {e}"

 @app.get("/predict")
 def display_prediction(message : str = "Hello World"):
     try:
+        dimention  = logistic_regression.get_label(message)
+        return dimention
     except Exception as e:
         return f"Unable to fetch the data {e}"

src/data/__pycache__/sample_data.cpython-312.pyc CHANGED Viewed

Binary files a/src/data/__pycache__/sample_data.cpython-312.pyc and b/src/data/__pycache__/sample_data.cpython-312.pyc differ

src/data/sample_data.py CHANGED Viewed

@@ -1,4 +1,5 @@
 import pandas as pd
 def get_data_frame(file_path):
     df = pd.read_excel(file_path)

 import pandas as pd
+from sentence_transformers import SentenceTransformer
 def get_data_frame(file_path):
     df = pd.read_excel(file_path)

src/modules/__pycache__/logistic_regression.cpython-312.pyc CHANGED Viewed

Binary files a/src/modules/__pycache__/logistic_regression.cpython-312.pyc and b/src/modules/__pycache__/logistic_regression.cpython-312.pyc differ

src/modules/logistic_regression.py CHANGED Viewed

@@ -20,7 +20,8 @@ def get_label(message):
     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

     models = LogisticRegression(max_iter=100)
     models.fit(X_train_embeddings, y_train)
     new_embeddings = model.encode(message)
+    no_of_dimention = len(new_embeddings)
     array = np.array(new_embeddings).tolist()
     # new_predictions = models.predict(new_embeddings)
     dimention = pd.DataFrame(array,columns=["Dimention"])
+    return {"Prediction_Dimention":{no_of_dimention: dimention}}