Update model.py

model.py

@@ -21,22 +21,24 @@ def get_prediction(college_name, course_name, category, gender, seat_level, roun
     return pipeline2.predict(template)
 
 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:
         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 = []
+    college_data = {}
     for college in colleges:
         prediciton = get_prediction(college, course, category, gender, seat_level, round)
+        if prediciton >= 100:
+             print(college, prediciton)
         if prediciton <= marks:
-            college_data.append(college + f"  predicted cut off = {prediciton}")
+            college_code = college.split("-")[0].strip()
+            college_name = " ".join(college.split("-")[1:]).strip()
+            # college_data.append(college + f"  predicted cut off = {prediciton}")
+            college_data[college_name] = {"college_code":college_code, "prediction": prediciton}
     return college_data 
 
 def get_courses_for_zone(zone):