Hot fixes for readme file

README.md

@@ -82,7 +82,8 @@ Here is an overview of our project structure:
 |   |   |--comp_spec_uml.png
 |   |-- Component Specification.md
 |   |-- Functional Specification.md
-|   |--ThroneTalks-TechnologyReview.pptx
+|   |--ThroneTalks-TechnologyReview.pdf
+|   |--Throne-Talk_final_presentation.pdf
 |--examples/
 |   |--images/
 |   |   |-- site_navigation_1.png
@@ -167,7 +168,7 @@ in a secrets.toml file within ```thronetalk-game-of-thrones-summarizer/.streamli
 
 <a id="examples"></a>
 ## Examples
-A video demonstration of our working application can be seen [here](https://drive.google.com/file/d/1ns3LBZTvtw00qaH_RWThXGwe9duF2EDC/view?usp=sharing).
+A video demonstration of our working application can be seen [here](https://drive.google.com/file/d/1GadkwGMEtFOznwmvRZYv9gkUYwnhFoLj/view?usp=sharing).
 
 More details on how to run our app can be found [here](./examples/README.md).
 

examples/README.md

@@ -63,4 +63,4 @@ The app is hosted as a Streamlit app here: **[throne-talk.streamlit.app](https:/
 <a id="web-application"></a>
 ## Web Application
 * Click [here](./site_navigation.md) for a website walk-through with text.
-* There is also a visual [Video Demonstration](https://drive.google.com/file/d/1ns3LBZTvtw00qaH_RWThXGwe9duF2EDC/view?usp=sharing).
+* There is also a visual [Video Demonstration](https://drive.google.com/file/d/1GadkwGMEtFOznwmvRZYv9gkUYwnhFoLj/view?usp=sharing).

thronetalk-game-of-thrones-summarizer/app.py

@@ -91,11 +91,11 @@ def remove_zeros(lst):
     characters = list(characters_tuple)
     st.subheader(out_text_temp2)
     st.markdown('''This line chart visualizes the ***sentiment scores*** across seasons and episodes
-                for the top 3 most active characters within the range selected by the user, 
-                based ***on-screen time***. Sentiment scores reflect the emotional tone of dialogue 
-                and actions, with positive scores indicating positive sentiment and negative scores 
-                indicating negative sentiment. This visualization allows users to track the 
-                emotional journey of key characters throughout the series, identifying trends, 
+                for the top 3 most active characters within the range selected by the user,
+                based ***on-screen time***. Sentiment scores reflect the emotional tone of dialogue
+                and actions, with positive scores indicating positive sentiment and negative scores
+                indicating negative sentiment. This visualization allows users to track the
+                emotional journey of key characters throughout the series, identifying trends,
                 patterns, and significant moments in their development.''')
     vg = VisualizationGenerator(
         int(season_from),
@@ -112,7 +112,7 @@ def remove_zeros(lst):
                         "fill": "white"
                         }
                             ).encode(
-                    x=alt.X('season-episode:O',title = 'Season:Episode'),
+                    x=alt.X('season-episode:O',title = 'Season:Episode', sort=None),
                     y=alt.Y('value:Q', title = 'Sentiment score'),
                     color='character name:N'
 
@@ -122,11 +122,11 @@ def remove_zeros(lst):
     out_text_2 = f"**{H2} {selected_episode_from} to {selected_episode_to}**"
     st.subheader(out_text_2)
     st.markdown('''These word clouds display the most frequently used words
-                 associated with the top 3 characters selected from the sentiment 
-                analysis above. Each word cloud visually represents the prominence 
-                of words in the dialogue or actions of these characters throughout 
-                the selected seasons and episodes. Larger words indicate higher 
-                frequency of use, offering users a glimpse into the key themes, 
+                 associated with the top 3 characters selected from the sentiment
+                analysis above. Each word cloud visually represents the prominence
+                of words in the dialogue or actions of these characters throughout
+                the selected seasons and episodes. Larger words indicate higher
+                frequency of use, offering users a glimpse into the key themes,
                 topics, and attributes associated with each character.''')
     columns = st.columns(len(characters))
     wordcloud = vg.multi_word_cloud(characters)