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app.py

@@ -9,6 +9,14 @@ app = Flask(__name__)
 zones = ['Amravati', 'Pune', 'Aurangabad', 'Mumbai & Thane', 'Konkan',
        'Nagpur', 'Nashik']
 
+seat_levels = ['State', 'Home', 'Other Than Home']
+
+categories = ['OPEN', 'SC', 'ST', 'VJ', 'NT1', 'NT2', 'NT3', 'OBC', 'DEFOPEN',
+       'TFW', 'DEFROBC', 'EW', 'PWDOPEN', 'PWDRSC', 'DEFRSC', 'PWDROBC',
+       'M', 'DEFOBC', 'DEFRNT1', 'DEFRNT2', 'ORPHA', 'PWDRVJ', 'PWDOBC',
+       'PWDRNT1', 'DEFRNT3', 'DEFRVJ', 'DEFSC', 'PWDRNT2', 'PWDSC',
+       'PWDRNT3', 'PWDRST', 'DEFRST', 'PWDROB']
+
 @app.route('/', methods=['GET', 'POST'])
 def index():
     colleges = []
@@ -17,11 +25,16 @@ def index():
         marks = float(request.form['marks'])
         zone = request.form['zone']
         course = request.form['course']
+        category = request.form['category']
+        gender = request.form['gender']
+        seat_level = request.form['seat_level']
+        round = 1
 
+        print(marks, zone, course, category, gender, seat_level, round)
         # Call the get_colleges function to predict the list of colleges
-        colleges = get_colleges(marks, zone, course)
+        colleges = get_colleges(marks, zone, course, category, gender, seat_level, round)
     
-    return render_template('index.html', colleges=colleges, zones = zones)
+    return render_template('index.html', colleges=colleges, zones = zones, categories = categories, seat_levels = seat_levels)
 
 @app.route('/get_courses', methods=['POST'])
 def get_courses():

model.py

@@ -7,30 +7,36 @@ with open("static/rf_pipeline.pkl", "rb") as f:
         pipeline2 = pickle.load(f)
 
 data = pd.read_csv("static/zone_course_and_colleges.csv")
+df = pd.read_csv("static/final_data_zone.csv")
 
-def get_prediction(college_name, course_name = "Computer Science and Engineering"):
+def get_prediction(college_name, course_name, category, gender, seat_level, round = 1):
     template = pd.DataFrame([[college_name, 
                               course_name, 
-                              "OPEN", 
-                              "General", 
-                              "State", 
+                              category, 
+                              gender, 
+                              seat_level, 
                               2024, 
-                              1]], 
+                              round]], 
                             columns=['college_name', 'course_name', 'category', 'gender', 'seat_level_attribute', 'year', 'round'])
     return pipeline2.predict(template)
 
-def get_colleges(marks, zone, course):
+def get_colleges(marks, zone, course, category, gender, seat_level, round):
     # data = pd.read_csv("static/zone_course_and_colleges.csv")
     # print(data[(data["zone"] == zone) & (data["course_name"] == course)]["colleges"].values[0])
     try:
-        colleges = list(eval(data[(data["zone"] == zone.strip()) & (data["course_name"] == course.strip())]["colleges"].values[0]))
-    except:
+        row = df[(df["zone"] == zone) & (df["course_name"] == course) & (df["category"] == category) & (df["gender"] == gender) & (df["seat_level_attribute"] ==  seat_level)]
+        if row.shape[0] == 0:
+             return ["No Seat Found"]
+        colleges = list(row["college_name"].unique())
+        # colleges = list(eval(data[(data["zone"] == zone.strip()) & (data["course_name"] == course.strip())]["colleges"].values[0]))
+    except Exception as e:
+         print("Exception", e)
          return ["Too less to get into"]
     college_data = []
     for college in colleges:
-        prediciton = get_prediction(college, course)
+        prediciton = get_prediction(college, course, category, gender, seat_level, round)
         if prediciton <= marks:
-            college_data.append(college)
+            college_data.append(college + f"predicted cut off = {prediciton}")
     return college_data 
 
 def get_courses_for_zone(zone):