lol pls work

Dockerfile

@@ -6,4 +6,4 @@ COPY . .
 
 RUN pip install --no-cache-dir -r requirements.txt
 
-CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

train.py

@@ -7,32 +7,12 @@ product_descriptions = pd.read_csv("./train.csv")
 product_descriptions = product_descriptions.dropna()
 
 vectorizer = TfidfVectorizer(stop_words='english')
-X1 = vectorizer.fit_transform(product_descriptions["value"])
+X1 = vectorizer.fit_transform(product_descriptions["product_descriptions"])
 
 true_k = 10
 model = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1)
 model.fit(X1)
 
-def show_recommendations(product):
-    Y = vectorizer.transform([product])
-    prediction = model.predict(Y)
-    return prediction
-
-def print_cluster(i):
-    for ind in order_centroids[i, :10]:
-        print(' %s' % terms[ind]),
-
-def get_cluster_terms(cluster_index):
-    cluster_terms = [terms[ind] for ind in order_centroids[cluster_index, :10]]
-    return cluster_terms
-
-order_centroids = model.cluster_centers_.argsort()[:, ::-1]
-terms = vectorizer.get_feature_names_out()
-
-print(print_cluster(show_recommendations("red dress")[0]))
-print(print_cluster(show_recommendations("water")[0]))
-print(print_cluster(show_recommendations("shoes")[0]))
-print(print_cluster(show_recommendations("cutting tool")[0]))
 
 pickle.dump(model, open("model.pkl", "wb"))
 pickle.dump(vectorizer, open("vectorizer.pkl", "wb"))