Frontend enhancement & algorithm imporvement

README.md

@@ -82,3 +82,27 @@ Ending Credits
 - Made using Python and Streamlit
 - @Author : [Sagar Bapodara](https://www.linkedin.com/in/sagar-bapodara/)
 
+
+# Contribution
+The contributed project is made on Flask Framework, which is combination of the the original project of Sagar Bapodara & the final year students of Shah and Anchor Kutchhi Engineering college.
+
+## 🔗 Contributors
+[[Nimit More]](https://github.com/nimitmore27)
+
+[[Shradha Jaiswal]](https://github.com/Jaiswalshradha)
+
+[[Shrushti Thakur]](https://github.com/shrushti0502)
+
+[[Hetal Paramar]](https://github.com/Hetsss)
+
+### Demo
+Clone this repository or Download the files into your local system. 
+
+- Extract the ZIP file (if you directly download from Github Web)
+- Make sure all the files are in the same folder/directory
+- Open your Command Prompt (CMD) in the same directory 
+- Type the following command (for web app) : 
+
+```bash
+python app.py
+```

app.py

@@ -0,0 +1,68 @@
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import linear_kernel
+from flask import Flask,request,render_template
+import pickle
+
+app=Flask(__name__)
+# Contributed Code
+with open("./models/combined_df.pkl","rb") as f:
+    combined_df=pickle.load(f)
+
+courses = combined_df[['Course Name', 'Course Description']].to_dict(orient='records')
+tfidf = TfidfVectorizer(stop_words='english')
+tfidf_matrix = tfidf.fit_transform([course['Course Description'] for course in courses])
+
+def get_recommendations(user_description, n=5):
+    recommended_courses = []
+    course_title = ''
+    user_vector = tfidf.transform([user_description])
+    cosine_sim = linear_kernel(user_vector, tfidf_matrix)
+    for idx in cosine_sim.argsort()[0][::-1][:n]:
+        course_title = courses[idx]['Course Name']
+        recommended_courses.append(course_title)
+    filtered_df = combined_df[combined_df['Course Name'].isin(recommended_courses)]
+    return filtered_df
+
+# Sagar Bapodara Code 
+courses_list = pickle.load(open('./models/courses.pkl','rb'))
+similarity = pickle.load(open('./models/similarity.pkl','rb'))
+
+def recommend(course):
+    index = courses_list[courses_list['course_name'] == course].index[0]
+    distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
+    recommended_course_names = []
+    for i in distances[1:7]:
+        course_name = courses_list.iloc[i[0]].course_name
+        recommended_course_names.append(course_name)
+
+    return recommended_course_names
+
+# Routings
+@app.route("/", methods=["GET","POST"])
+def index():
+    if request.method == "POST":
+        des = request.form.get("des")
+        num = request.form.get("num")
+        if not num:
+            num = 5
+        recommendations = get_recommendations(des,int(num))
+        params={"title":"Home", "result":{'active':True,"data" : recommendations}}
+        return render_template("index.html", params=params)
+    params={"title":"Home","result":{'active':False}}
+    return render_template("index.html", params=params)
+
+
+@app.route('/SagarBapodara',methods=['GET','POST'])
+def main():
+    course_list = courses_list['course_name'].values
+    if request.method == "POST":
+        des = request.form.get("des")
+        recommended_course_names = recommend(des)
+        params={"title":"Home", "result":{'active':True,"data" : recommended_course_names},"course_list":course_list}
+        return render_template('sagar.html',params=params)
+    params = {"titile":"Sagar Bapodara's Project", "result":{'active':False}, "course_list":course_list}
+    return render_template('sagar.html',params=params)
+
+
+if __name__ == "__main__":
+    app.run(debug=True)