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+ARG PYTHON_VERSION=3.10
+FROM python:${PYTHON_VERSION}-slim as base
+
+# Prevents Python from writing pyc files.
+ENV PYTHONDONTWRITEBYTECODE=1
+
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+ARG UID=10001
+RUN adduser \
+    --disabled-password \
+    --gecos "" \
+    --home "/nonexistent" \
+    --shell "/sbin/nologin" \
+    --no-create-home \
+    --uid "${UID}" \
+    appuser
+
+RUN --mount=type=cache,target=/root/.cache/pip \
+    --mount=type=bind,source=requirements.txt,target=requirements.txt \
+    python -m pip install -r requirements.txt
+
+# Switch to the non-privileged user to run the application.
+USER appuser
+
+# Copy the source code into the container.
+COPY . .
+COPY . .env 
+
+# Expose the port that the application listens on.
+EXPOSE 8000
+
+# Run the application.
+CMD ["gunicorn", "app:app", "-b", "0.0.0.0:8000"]

app.py

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+from flask import Flask, render_template, request, jsonify
+from model import get_colleges, get_courses_for_zone  # Import the model function
+
+import pandas as pd
+import pickle
+
+app = Flask(__name__)
+
+zones = ['Amravati', 'Pune', 'Aurangabad', 'Mumbai & Thane', 'Konkan',
+       'Nagpur', 'Nashik']
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    colleges = []
+    if request.method == 'POST':
+        # Get data from the form
+        marks = float(request.form['marks'])
+        zone = request.form['zone']
+        course = request.form['course']
+
+        # Call the get_colleges function to predict the list of colleges
+        colleges = get_colleges(marks, zone, course)
+    
+    return render_template('index.html', colleges=colleges, zones = zones)
+
+@app.route('/get_courses', methods=['POST'])
+def get_courses():
+    zone = request.json['zone']
+    courses = get_courses_for_zone(zone)
+    courses = courses.tolist()
+    return jsonify(courses)
+
+if __name__ == '__main__':
+    app.run(debug=True)

model.py

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+import pandas as pd
+import pickle 
+import warnings
+warnings.filterwarnings("ignore")
+
+with open("static/rf_pipeline.pkl", "rb") as f:
+        pipeline2 = pickle.load(f)
+
+data = pd.read_csv("static/zone_course_and_colleges.csv")
+
+def get_prediction(college_name, course_name = "Computer Science and Engineering"):
+    template = pd.DataFrame([[college_name, 
+                              course_name, 
+                              "OPEN", 
+                              "General", 
+                              "State", 
+                              2024, 
+                              1]], 
+                            columns=['college_name', 'course_name', 'category', 'gender', 'seat_level_attribute', 'year', 'round'])
+    return pipeline2.predict(template)
+
+def get_colleges(marks, zone, course):
+    # 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:
+         return ["Too less to get into"]
+    college_data = []
+    for college in colleges:
+        prediciton = get_prediction(college, course)
+        if prediciton <= marks:
+            college_data.append(college)
+    return college_data 
+
+def get_courses_for_zone(zone):
+    row = data[data["zone"] == zone.strip()]
+    courses = row["course_name"].unique()
+    return courses

requirements.txt

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+flask
+requests
+pandas
+gunicorn
+python-dotenv
+scikit-learn