diff --git a/requirements.txt b/requirements.txt
index 43c5d24..932bb22 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,5 +6,7 @@ pytube==11.0.1
 SpeechRecognition==3.8.1
 streamlit==0.89.0
 sumy
+monkeylearn
 punctuator==0.9.6
 wget
+
diff --git a/source/scrivener_user_interface.py b/source/scrivener_user_interface.py
index 5c0526d..21bd62a 100644
--- a/source/scrivener_user_interface.py
+++ b/source/scrivener_user_interface.py
@@ -14,6 +14,8 @@
 from main.transcribe_yt import TranscribeYtVideo
 import secrets
 from glob import glob
+from monkeylearn import MonkeyLearn
+
 
 import shutil
 
@@ -26,7 +28,7 @@
 st.markdown(hide_menu_style, unsafe_allow_html=True)
 
 # Add footer to UI
-footer="""<style>
+footer = """<style>
 a:link , a:visited{
 color: blue;
 background-color: transparent;
@@ -40,7 +42,7 @@
 }
 
 .footer {
-position: fixed;
+
 left: 0;
 bottom: 0;
 width: 100%;
@@ -55,14 +57,20 @@
 <p>Contributors: Anshul, Bhavya, Darshan, Pragna, Rohan</p>
 </div>
 """
-st.markdown(footer,unsafe_allow_html=True)
+st.markdown(footer, unsafe_allow_html=True)
 
 # Check if ML model files have been combined, if not combine them
 # This needs to be done because the full file is greater than 100mb
 # and GitHub does not allow files larger than 100mb to be pushed
-if not os.path.exists('source/punct_model_full.pcl'):
+if not os.path.exists("source/punct_model_full.pcl"):
     print("Creating punct_model_full.pcl file for ML model...")
 
+    first_file = os.path.abspath("source/punct_model_part1.pcl")
+    second_file = os.path.abspath("source/punct_model_part2.pcl")
+    third_file = os.path.abspath("source/punct_model_part3.pcl")
+    new_file = os.path.abspath("source/punct_model_full.pcl")
+
+
 
     # Path to model files parts that needs to be combined
     # Storing these models in github causes an issue with the Heroku deployment and exceeds 500 MB (it is 618 MB)
@@ -93,6 +101,7 @@
     # Path to combined model file
     new_file = os.path.abspath('source/punct_model_full.pcl')
 
+
     # Read content of model file parts and write it to the combined model file
     with open(new_file, "wb") as wfd:
         for f in [first_file, second_file, third_file]:
@@ -102,35 +111,42 @@
 
 # Download the uploaded video file
 def save_file(file):
-    with open(os.path.join(os.getcwd(), file.name), 'wb') as f:
+    with open(os.path.join(os.getcwd(), file.name), "wb") as f:
         f.write(file.getbuffer())
-    return 
+    return
+
 
 # Display Image
 st.image("media/logo/logo.gif")
 
 # Display Radio options
-input_format = st.radio('Choose your input format', ['Youtube Link', 'Upload a Video'])
+input_format = st.radio("Choose your input format", ["Youtube Link", "Upload a Video"])
 
 # If user provides a Youtube Link
-if input_format=='Youtube Link':
-    # Text input box 
-    youtube_link = st.text_input('Enter Youtube Link')
+if input_format == "Youtube Link":
+    # Text input box
+    youtube_link = st.text_input("Enter Youtube Link")
     # Check if its a valid youtube link
-    if re.findall('(www\.youtube\.com\/watch\?v=)',youtube_link):
+    if re.findall("(www\.youtube\.com\/watch\?v=)", youtube_link):
         st.video(youtube_link)
         # Make a progress bar
         progress_bar = st.progress(0)
         # Decorative material
-        progress_lines = secrets.choice(['Hired Shakespeare to summarize your video', 'Taking advice from Charles Dickens to help you',
-                                        'Shakespeare is completing the assignment', 'Do not worry, Mark Twain is on it',
-                                        'Robert Frost is taking the right road to summarize your video'])
+        progress_lines = secrets.choice(
+            [
+                "Hired Shakespeare to summarize your video",
+                "Taking advice from Charles Dickens to help you",
+                "Shakespeare is completing the assignment",
+                "Do not worry, Mark Twain is on it",
+                "Robert Frost is taking the right road to summarize your video",
+            ]
+        )
         progress_bar.progress(10)
-        
+
         # Wait till we run the summarization
-        with st.spinner(progress_lines+' . . .'):
+        with st.spinner(progress_lines + " . . ."):
             progress_bar.progress(25)
-            # Call TranscribeYtVideo class 
+            # Call TranscribeYtVideo class
             transcribe_video = TranscribeYtVideo(youtube_link)
             progress_bar.progress(40)
             # Get summary
@@ -139,42 +155,73 @@ def save_file(file):
         # Complete progress bar to 100
         progress_bar.progress(100)
         # Display Summary
-        st.header('Summary')
+        st.header("Summary")
         st.write(summary)
 
+        data = " ".join(summary.splitlines()[-10:])
+        st.header("Sentiment Analysis")  # sentiment analysis using monkey learn API
+        ml = MonkeyLearn("4f627e517819c240ab01baa82f6976153f0817d1")
+        model_id = "cl_pi3C7JiL"
+        result = ml.classifiers.classify(model_id, [data])
+        for entry in result.body:
+            for classification in entry["classifications"]:
+                tag_name = classification.get("tag_name")
+                if tag_name is not None:
+                    st.write(tag_name)
+
+
     # If user inputs an invalid Youtube link
-    elif youtube_link!='':
-        st.error('Please enter a valid Youtube Link!')
+    elif youtube_link != "":
+        st.error("Please enter a valid Youtube Link!")
 
-# If user uploads a local video    
-elif input_format=='Upload a Video':
+# If user uploads a local video
+elif input_format == "Upload a Video":
     # Browse button for uploading .mp4 files
-    file = st.file_uploader('Upload a video',type=['mp4'],accept_multiple_files=False)
+    file = st.file_uploader("Upload a video", type=["mp4"], accept_multiple_files=False)
     if file is not None:
         st.video(file)
         # Make a progress bar
         progress_bar = st.progress(0)
         progress_bar.progress(10)
         # Decorative material
-        progress_lines = secrets.choice(['Hired Shakespeare to summarize your video', 'Taking advice from Charles Dickens to help you',
-                                        'Shakespeare is completing the assignment', 'Do not worry, Mark Twain is on it',
-                                        'Robert Frost is taking the right road to summarize your video'])
+        progress_lines = secrets.choice(
+            [
+                "Hired Shakespeare to summarize your video",
+                "Taking advice from Charles Dickens to help you",
+                "Shakespeare is completing the assignment",
+                "Do not worry, Mark Twain is on it",
+                "Robert Frost is taking the right road to summarize your video",
+            ]
+        )
         # Wait till we run the summarization
-        with st.spinner(progress_lines+' . . .'):
+        with st.spinner(progress_lines + " . . ."):
             progress_bar.progress(25)
             # Download the uploaded video file
             save_file(file)
             progress_bar.progress(40)
-            # Call TranscribeVideo class 
+            # Call TranscribeVideo class
             transcribe_video = TranscribeVideo()
             progress_bar.progress(60)
             # Get summary
-            summary = transcribe_video.transcribe_video(os.path.join(os.getcwd(), file.name))
+            summary = transcribe_video.transcribe_video(
+                os.path.join(os.getcwd(), file.name)
+            )
         # Complete progress bar to 100
         progress_bar.progress(100)
         # Display Summary
-        st.header('Summary')
+        st.header("Summary")
         st.write(summary)
+        data = " ".join(summary.splitlines()[-10:])
+        st.header("Sentiment Analysis")  # sentiment analysis using monkey learn API
+        ml = MonkeyLearn("4f627e517819c240ab01baa82f6976153f0817d1")
+        model_id = "cl_pi3C7JiL"
+        result = ml.classifiers.classify(model_id, data)
+        for entry in result.body:
+            for classification in entry["classifications"]:
+                tag_name = classification.get("tag_name")
+                if tag_name is not None:
+                    st.write(tag_name)
+
     else:
-        for name in glob('*.mp4'):
+        for name in glob("*.mp4"):
             os.remove(name)