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Dockerfile

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+FROM python:3.8
+
+WORKDIR /deeplearning_models_heroku
+ADD models /deeplearning_models_heroku/models
+ADD streamlit_app.py .
+
+RUN pip install numpy tensorflow-cpu opencv-python-headless streamlit Pillow scikit-learn pandas pickleshare keras requests flask
+
+CMD ["streamlit", "run" ,"./streamlit_app.py"] 

LICENSE

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+MIT License
+
+Copyright (c) 2022 Mert
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Procfile

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+web: sh setup.sh && streamlit run streamlit_app.py

catvsdog.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+import cv2
+from PIL import Image
+
+def main_catvsdog():
+    
+    st.header("Cats Vs Dogs")
+    model = tf.keras.models.load_model("models/catsVSdogs.h5")
+
+    image_file = st.file_uploader(
+        "Upload image for testing", type=['jpeg', 'png', 'jpg', 'webp'])
+
+    if st.button("Process"):
+        image = Image.open(image_file)
+        #image = cv2.imread (image_file)
+        image = np.array(image.convert('RGB'))
+        image = cv2.resize(image, (224, 224))
+        image = np.reshape(image, [1, 224, 224, 3])
+
+        FRAME_WINDOW = st.image([])
+
+        classes = model.predict(image)
+        if classes > 0.5:
+            st.header("Dog")
+            st.subheader(classes)
+        if classes < 0.5:
+            st.header("Cat")
+            st.subheader(1-classes)
+
+        image1 = Image.open(image_file)
+        FRAME_WINDOW.image(image1)
+        
+
+if __name__ == '__main__':
+    main_catvsdog()	   

disaster_twet.py

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+import pandas as pd
+import streamlit as st 
+import pickle
+from sklearn import model_selection
+from sklearn.feature_extraction.text import CountVectorizer
+
+def main_twet():
+
+    st.header("Disaster Tweet Classification")
+    filename = 'models/finalized_model.sav'
+    loaded_model = pickle.load(open(filename, 'rb'))
+    data1 = pd.read_csv("models/data1.csv")
+
+    train_x, test_x, train_y, test_y = model_selection.train_test_split(data1["text"],
+                                                                            data1["target"], random_state=42)
+
+    sentences = ["Just happened a terrible car crash",
+                     "We're shaking...It's an earthquake",
+                     "there is a forest fire at spot pond, geese are fleeing across the street, I cannot save them all",
+                     "Paradise ,the bitches say im hot i say no bitch im blazing",
+                     "Refugio oil spill may have been costlier bigger than projected",
+                     "someone hold my hand and tell me ITS OKAY because I am having a panic attack for no reason"
+                     ]
+
+    option = st.selectbox(
+            'You can select here', sentences)
+
+    if st.button("Process from select box"):
+        option = pd.Series(option)
+        vectorizer = CountVectorizer()
+        vectorizer.fit(train_x)
+        option = vectorizer.transform(option)
+        result = loaded_model.predict(option)
+        if result == 1:
+            st.header("Disaster")
+        if result == 0:
+            st.header("Not Disaster")
+
+    input = st.text_input("Custom text")
+
+    if st.button("Process custom text"):
+        input = pd.Series(input)
+        vectorizer = CountVectorizer()
+        vectorizer.fit(train_x)
+        input = vectorizer.transform(input)
+        result = loaded_model.predict(input)
+
+        if result == 1:
+            st.header("Disaster")
+
+        if result == 0:
+            st.header("Not Disaster")
+            
+
+if __name__ == '__main__':
+    main_twet()	

face_detec.py

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+import streamlit as st 
+import cv2
+from PIL import Image,ImageEnhance
+import numpy as np 
+import os
+
+face_cascade = cv2.CascadeClassifier('models/haarcascade_frontalface_default.xml')
+eye_cascade = cv2.CascadeClassifier('models/haarcascade_eye.xml')
+smile_cascade = cv2.CascadeClassifier('models/haarcascade_smile.xml')
+
+@st.cache
+def load_image(img):
+	im = Image.open(img)
+	return im
+
+def detect_faces(our_image):
+    new_img = np.array(our_image.convert('RGB'))
+    img = cv2.cvtColor(new_img,1)
+    gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
+    # Detect faces
+    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+    # Draw rectangle around the faces
+    for (x, y, w, h) in faces:
+        cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
+    return img,faces 
+
+def main_face():
+	
+	st.title("Face Detection App")
+	image_file = st.file_uploader("Upload Image",type=['jpg','png','jpeg'])
+
+	if image_file is not None:
+		our_image = Image.open(image_file)
+		st.text("Original Image")
+		# st.write(type(our_image))
+		st.image(our_image,width=300)
+		
+		if st.button("Process"):
+			result_img,result_faces = detect_faces(our_image)
+			st.image(result_img)
+
+			st.success("Found {} faces".format(len(result_faces)))
+			#elif feature_choice == 'Smiles':
+			#	result_img = detect_smiles(our_image)
+			#	st.image(result_img)
+
+if __name__ == '__main__':
+	main_face()	

movie_rec.py

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+import _pickle as cPickle
+import bz2
+import pickle
+import streamlit as st
+import requests
+
+def decompress_pickle(file):
+    data = bz2.BZ2File(file, "rb")
+    data = cPickle.load(data)
+    return data
+
+movies = pickle.load(open('models/movie_list.pkl', 'rb'))
+similarity = decompress_pickle('models/similarity.pbz2')
+
+def fetch_poster(movie_id):
+
+    url = "https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US".format(
+        movie_id)
+    data = requests.get(url)
+    data = data.json()
+    poster_path = data['poster_path']
+    full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
+    return full_path
+
+
+def recommend(movie):
+    index = movies[movies['title'] == movie].index[0]
+    distances = sorted(
+        list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
+    recommended_movie_names = []
+    recommended_movie_posters = []
+    for i in distances[1:6]:
+        # fetch the movie poster
+        movie_id = movies.iloc[i[0]].movie_id
+        recommended_movie_posters.append(fetch_poster(movie_id))
+        recommended_movie_names.append(movies.iloc[i[0]].title)
+
+    return recommended_movie_names, recommended_movie_posters
+
+def main_movie() :
+    
+    st.header('Movie Recommender System')
+    movie_list = movies['title'].values
+    selected_movie = st.selectbox(
+            "Type or select a movie from the dropdown",
+            movie_list
+        )
+
+    if st.button('Show Recommendation'):
+        recommended_movie_names, recommended_movie_posters = recommend(selected_movie)
+        col1, col2, col3, col4, col5 = st.columns(5)
+        with col1:
+            st.text(recommended_movie_names[0])
+            st.image(recommended_movie_posters[0])
+        with col2:
+            st.text(recommended_movie_names[1])
+            st.image(recommended_movie_posters[1])
+        with col3:
+            st.text(recommended_movie_names[2])
+            st.image(recommended_movie_posters[2])
+        with col4:
+            st.text(recommended_movie_names[3])
+            st.image(recommended_movie_posters[3])
+        with col5:
+            st.text(recommended_movie_names[4])
+            st.image(recommended_movie_posters[4])
+      
+      
+
+if __name__ == '__main__':
+    main_movie()	      
+            

requirements.txt

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+numpy ==  1.23.1
+tensorflow-cpu==2.9.1
+opencv-python-headless==4.4.0.42
+streamlit ==  1.11.0
+Pillow== 9.2.0
+scikit-learn==0.23.1
+pandas == 1.4.3
+pickleshare ==  0.7.5
+keras == 2.9.0
+requests ==  2.24.0
+tensorflow-hub == 0.12.0

runtime.txt

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+python-3.8.8

setup.sh

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+mkdir -p ~/.streamlit/
+
+echo "\
+[server]\n\
+port = $PORT\n\
+enableCORS = false\n\
+headless = true\n\
+\n\
+" > ~/.streamlit/config.toml

streamlit_app.py

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+import streamlit as st
+import os
+import pandas as pd
+from face_detec import main_face 
+from movie_rec import main_movie
+from disaster_twet import main_twet
+from catvsdog import main_catvsdog
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+# -------------------------------------------------------------------------------------------------------------
+def main():
+    st.set_page_config(layout="wide")
+    st.write("")
+    st.sidebar.write("")
+    st.sidebar.write("")
+    st.sidebar.write("")
+    st.sidebar.write("")
+    st.sidebar.subheader("Select an option")
+    activities = [
+        "Cats vs Dogs", "Disaster Tweet Classification", "Movie Recommender", "Face Detection"]
+    choice = st.sidebar.selectbox("", activities)
+
+# ------------Cats Vs Dogs ----------------------------------------------------------------
+
+    if choice == "Cats vs Dogs":
+        main_catvsdog()
+# ------------------------------------------------------------------------
+    if choice == "Disaster Tweet Classification":
+        main_twet()
+
+# ----------------------------------------------------------------------------------------------------------------
+    if choice == "Movie Recommender":
+        main_movie()
+# -------------------------------------------------------------------------------
+    if choice == "Face Detection": 
+        main_face()
+
+if __name__ == '__main__':
+    main()