Create app.py

app.py

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+import pandas as pd
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+from typing_extensions import Doc
+import gradio as gr
+df = pd.read_csv('dataframe.csv')
+tfidf_matrix = pd.read_csv('tfidf_matrix.csv', header=None).values
+tfidf_matrix.shape
+word2vec_matrix = pd.read_csv('word2vecmatrix.csv',header=None).values
+word2vec_matrix.shape
+
+sbert1_matrix = pd.read_csv('sentencetransformer1.csv',header=None).values
+sbert1_matrix.shape
+
+sbert2_matrix = pd.read_csv('sentencetransformer2 copy.csv',header=None).values
+sbert2_matrix.shape
+
+def course_recommendation(model, course_subject_code, course_number, whether_not_lower_level=False, whether_only_sameorlower_level = False, whether_not_same_subject=False, whether_only_same_subject=False, recomendations_number = 5):
+    if model == "tf-idf":
+        docmatrix = tfidf_matrix
+    elif model == "word2vec":
+        docmatrix = word2vec_matrix
+    elif model == "sbert1":
+        docmatrix = sbert1_matrix  # This appears to have been a typo in the original code
+    elif model == "sbert2":
+        docmatrix = sbert2_matrix
+
+    # Check if the course exists in the dataframe
+    if not ((df['Course Subject Code'] == course_subject_code) & (df['Course Number'] == course_number)).any():
+        return pd.DataFrame({'Message': ["The course you input does not exist in this semester or we do not have enough course description information about it. Please try another course. "]})
+
+    if whether_not_lower_level == True and whether_only_sameorlower_level == True:
+        return pd.DataFrame({'Message': ["There seems to be a conflict in the filtering logic. Please double-check the checkboxes for filtering carefully."]})
+    if whether_not_same_subject == True and whether_only_same_subject == True:
+        return pd.DataFrame({'Message': ["There seems to be a conflict in the filtering logic. Please double-check the checkboxes for filtering carefully."]})
+
+    # Get the index and level of the course in the dataframe
+    course_info = df[(df['Course Subject Code'] == course_subject_code) & (df['Course Number'] == course_number)]
+    course_index = course_info.index[0]
+    course_level = course_info.iloc[0]['Course Level']
+    # Normalize "First-year Student Seminar" to "100-level"
+    course_level = "100-level" if course_level == "First-year Student Seminar" else course_level
+
+    df_filtered = df.copy()
+    if whether_not_same_subject:
+        df_filtered = df_filtered[df_filtered['Course Subject Code'] != course_subject_code]
+    if whether_only_same_subject:
+        df_filtered = df_filtered[df_filtered['Course Subject Code'] == course_subject_code]
+    
+    if whether_not_lower_level:
+        levels_to_include = ['100-level', '200-level', '300-level', '400-level', 'Graduate level']
+        current_level_index = levels_to_include.index(course_level)
+        allowed_levels = levels_to_include[current_level_index:]  # Include current and higher levels
+        df_filtered = df_filtered[df_filtered['Course Level'].isin(allowed_levels)]
+    
+    if whether_only_sameorlower_level:
+        levels_to_include = ['100-level', '200-level', '300-level', '400-level', 'Graduate level']
+        current_level_index = levels_to_include.index(course_level)
+        allowed_levels = levels_to_include[:current_level_index + 1]  # Include current and lower levels
+        df_filtered = df_filtered[df_filtered['Course Level'].isin(allowed_levels)]
+
+    # Retrieve the vector for the specified course
+    course_vector = docmatrix[course_index]
+
+    # Calculate the cosine similarity with filtered courses
+    cosine_similarities = cosine_similarity(docmatrix[df_filtered.index], course_vector.reshape(1, -1)).flatten()
+
+    # Get the indices of the courses with the highest cosine similarity scores
+    similar_courses_indices = np.argsort(-cosine_similarities)[:int(recomendations_number)+1]
+
+    # Retrieve the course details for the most similar courses
+    similar_courses = df_filtered.iloc[similar_courses_indices][['Course Code', 'Course Title', 'Course Description Text']]
+    if similar_courses.index[0] == course_index:
+        similar_courses = similar_courses.iloc[1:]  # Exclude the original course if it is the highest ranked
+    else:
+        similar_courses = similar_courses.head(int(recomendations_number))
+    
+    # Insert a column for similarity rank
+    
+    input_course_details = course_info[['Course Code', 'Course Title', 'Course Description Text']]
+    result_df = pd.concat([input_course_details, similar_courses]).reset_index(drop=True)
+    result_df .insert(0, 'Similar Rank', range(0, len(similar_courses) + 1))
+    return result_df
+
+import gradio as gr
+import pandas as pd
+from functools import partial
+
+def highlight_first_row(s, props=''):
+    return [props if s.name == 0 else '' for _ in range(len(s))]
+
+def recommend(model_name, course_subject_code, course_number, exclude_lower_levels, exclude_upper_levels, exclude_same_subject, exclude_other_subject, recomendations_number):
+    outputdf = course_recommendation(model_name, course_subject_code, course_number, exclude_lower_levels, exclude_upper_levels, exclude_same_subject, exclude_other_subject, recomendations_number)
+    outputdf = outputdf.style.apply(highlight_first_row, props='background-color: orange;', axis=1)
+    return outputdf
+
+
+
+def main():
+    with gr.Blocks(theme=gr.themes.Default(primary_hue="blue")) as demo:
+        gr.Markdown("# Course Recommendation System - For UIUC fall 2024 semester")
+        gr.Markdown("This project provides course recommendations using different NLP models. Select a model and enter course details to see recommendations.")
+        gr.Markdown("Want to know how these models work? Check out the **ABOUT** tab:)")
+        with gr.Row():
+            with gr.Column(scale=2):
+                gr.Markdown("*Choose the course you want to explore:*" )
+                with gr.Row():
+                    subject = gr.Dropdown(choices=sorted(df['Course Subject Code'].unique()), label="Course Subject Code")
+                    number = gr.Textbox(label="Course Number")
+                recommendation_no = gr.Slider(3, 100, step = 1, label="Recommendation Number", info="Choose between 3 and 100")
+            with gr.Column(scale=1):
+                gr.Markdown("*You may want to add a filter:*")
+                with gr.Row():
+                    exclude_lower = gr.Checkbox(label="Only Upper Level", info = "Same level and higher level courses will be shown")
+                    exclude_upper = gr.Checkbox(label="Only Lower Level", info = "Same level and lower level courses will be shown")
+                with gr.Row():
+                    exclude_same = gr.Checkbox(label="Only Different Subject")
+                    exclude_other = gr.Checkbox(label="Only Same Subject")
+                tf_idf_submit = gr.Button("Recommend", variant="primary")
+        with gr.Tabs() as tabs:
+            
+            # Setting up the interface for each model
+            with gr.Tab("Word2Vec Model"):
+                tf_idf_submit.click(
+                    fn=partial(recommend, "word2vec"),
+                    inputs=[subject, number, exclude_lower, exclude_upper, exclude_same, exclude_other, recommendation_no],
+                    outputs=gr.Dataframe(wrap = True, column_widths = ["10%","10%", "20%", "63%"])
+                )
+            with gr.Tab("TF-IDF Model"):
+                tf_idf_submit.click(
+                    fn=partial(recommend, "tf-idf"),
+                    inputs=[subject, number, exclude_lower, exclude_upper, exclude_same, exclude_other, recommendation_no],
+                    outputs=gr.Dataframe(wrap = True, column_widths = ["10%","10%", "20%", "63%"])
+                )
+            with gr.Tab("SBERT Model1"):
+                tf_idf_submit.click(
+                    fn=partial(recommend, "sbert1"),
+                    inputs=[subject, number, exclude_lower, exclude_upper, exclude_same, exclude_other, recommendation_no],
+                    outputs=gr.Dataframe(wrap = True, column_widths = ["10%","10%", "20%", "63%"])
+                )
+            with gr.Tab("SBERT Model2"):
+                tf_idf_submit.click(
+                    fn=partial(recommend, "sbert2"),
+                    inputs=[subject, number, exclude_lower, exclude_upper, exclude_same, exclude_other, recommendation_no],
+                    outputs=gr.Dataframe(wrap = True, column_widths = ["10%","10%", "20%", "63%"])
+                )
+            with gr.Tab("ABOUT"):
+                gr.Markdown("This project provides course recommendations using different NLP models. Select a model and enter course details to see recommendations.")
+    return demo
+
+# Launch the interface
+if __name__ == "__main__":
+    main().launch(share=True)
+