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import streamlit as st
import pickle
import string
from nltk.corpus import stopwords
import nltk
from nltk.stem.porter import PorterStemmer
import sklearn

ps = PorterStemmer()

def Datapreprocessing(text):
text = text.lower()
text = nltk.word_tokenize(text)

y = []

for i in text:
if i.isalnum():
y.append(i)
text = y.copy()
y.clear()

for i in text:
if i not in string.punctuation and i not in stopwords.words('english'):
y.append(i)

text = y.copy()
y.clear()

for i in text:
y.append(ps.stem(i))

text = y[:]
y.clear()

text = " ".join(text)
return text

tfidf = pickle.load(open('vectorizer.pkl','rb'))
model = pickle.load(open('model.pkl','rb'))

st.title('Email/SMS Spam Classifier ')


input_sms = st.text_area('Enter the EmailSMS : ')

if st.button('Predict'):

# 1. preprocess
transformed_sms = Datapreprocessing(input_sms)
# 2. vectorize
vector_input = tfidf.transform([transformed_sms])
# 3. predict
result = model.predict(vector_input)[0]
# 4. Display
if result == 1:
st.header("Spam ! Be Careful AMIGO ;)")
else:
st.header("Not Spam ! Go ahead buddy :D ")

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tmdb_5000_credits.csv filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1 @@
+venv

Procfile

@@ -0,0 +1 @@
+web: sh setup.sh && streamlit run app.py

main.py

@@ -0,0 +1,58 @@
+import pandas as pd
+import streamlit as st
+import pickle
+import requests
+
+def fetch_image(movie_id):
+    response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US'.format(movie_id))
+
+    data = response.json()
+
+    return "https://image.tmdb.org/t/p/w500/" + data['poster_path']
+
+def recommand(movie):
+    movie_index = movies[movies['title'] == movie].index[0]
+    distences = similarity[movie_index]
+    movies_list = sorted(list(enumerate(distences)),reverse=True,key = lambda x : x[1])[1:6]
+
+    recommanded_movie = []
+    recommaned_poster=[]
+    for i in movies_list:
+        movie_id = movies.iloc[i[0]].movie_id
+
+        recommanded_movie.append(movies.iloc[i[0]].title)
+        recommaned_poster.append(fetch_image(movie_id)) # Fetch poster from API
+    return recommanded_movie,recommaned_poster
+
+movies_dict = pickle.load(open('movie_dict.pkl','rb'))
+movies = pd.DataFrame(movies_dict)
+
+similarity = pickle.load(open('similarity.pkl','rb'))
+
+st.title('Movie Recommender System')
+
+selected_movie = st.selectbox(
+    'What is your taste in movie ? ',
+    movies['title'].values)
+st.write('You selected:', selected_movie)
+
+
+if st.button('Recommend movie'):
+    name,poster =recommand(selected_movie)
+
+    col1, col2, col3, col4, col5 = st.columns(5)
+    with col1:
+        st.text(name[0])
+        st.image(poster[0])
+    with col2:
+        st.text(name[1])
+        st.image(poster[1])
+    with col3:
+        st.text(name[2])
+        st.image(poster[2])
+    with col4:
+        st.text(name[3])
+        st.image(poster[3])
+    with col5:
+        st.text(name[4])
+        st.image(poster[4])

movie_dict.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14df69a1d4c566857cdca3fc533ce3bd2f9867c13517964b7b4fb64a5fe39e1c
+size 2216684

setup.sh

@@ -0,0 +1,9 @@
+mkdir -p ~/.streamlit/
+
+echo  "\
+[server]\n\
+port = $PORT\n\
+enableCORS = false\n\
+headless = true\n\
+\n\
+" > ~/.streamlit/confit.toml

similarity.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3cdddefcba2492c64942a441915bc409e2449bbea1787f60d58535c47732973
+size 184781251

tmdb_5000_credits.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d0050599ff88d40366c4841204b1489862bca346bfa46c20b05a65d14508435
+size 40044293