main.py

from huggingface_hub import InferenceClient

import random

from flask import Flask, request, jsonify, redirect, url_for
from flask_cors import CORS
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")

connection_string = "postgresql://data_owner:PFAnX9oJp4wV@ep-green-heart-a78sxj65.ap-southeast-2.aws.neon.tech/figurecircle?sslmode=require"

engine = create_engine(connection_string)
Session = sessionmaker(bind=engine)

app = Flask(__name__)
CORS(app)
    
@app.route('/')
def home():
    return jsonify({"message": "Welcome to the Recommendation API!"})


def format_prompt(message):
    # Generate a random user prompt and bot response pair
    user_prompt = "UserPrompt"
    bot_response = "BotResponse"

    return f"<s>[INST] {user_prompt} [/INST] {bot_response}</s> [INST] {message} [/INST]"


@app.route('/ai_mentor', methods=['POST'])
def ai_mentor():
    data = request.get_json()
    message = data.get('message')

    if not message:
        return jsonify({"message": "Missing message"}), 400

    temperature = 0.9
    max_new_tokens = 256
    top_p = 0.95
    repetition_penalty = 1.0

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    # Define prompt for the conversation
    prompt = f""" prompt:
     Act as an mentor
    User: {message}"""

    formatted_prompt = format_prompt(prompt)

    try:
        # Generate response from the Language Model
        response = client.text_generation(formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False)

        return jsonify({"response": response}), 200
    except Exception as e:
        return jsonify({"message": f"Failed to process request: {str(e)}"}), 500


@app.route('/get_course', methods=['POST'])
def get_course():
    temperature = 0.9
    max_new_tokens = 256
    top_p = 0.95
    repetition_penalty = 1.0

    content = request.json
    # user_degree = content.get('degree')  # Uncomment this line
    user_stream = content.get('stream')

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )
    prompt = f""" prompt: 
    You need to act like as recommendation engine for course recommendation for a student. Below are current details.
    Degree: {user_degree}
    Stream: {user_stream}
    Based on current details recommend the courses for higher degree.
    Note: Output should be list in below format:
    [course1, course2, course3,...]
    Return only answer not prompt and unnecessary stuff, also dont add any special characters or punctuation marks
    """
    formatted_prompt = format_prompt(prompt)

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False)
    return jsonify({"ans": stream})


@app.route('/get_mentor', methods=['POST'])
def get_mentor():
    temperature = 0.9
    max_new_tokens = 256
    top_p = 0.95
    repetition_penalty = 1.0

    content = request.json
    # user_degree = content.get('degree')  # Uncomment this line
    user_stream = content.get('stream')
    courses = content.get('courses')

    session = Session()

    # Query verified mentors
    verified_mentors = session.query(Mentor).filter_by(verified=True).all()

    mentor_list = [{"id": mentor.id, "mentor_name": mentor.mentor_name, "skills": mentor.skills,
                    "qualification": mentor.qualification, "experience": mentor.experience,
                    "verified": mentor.verified} for mentor in verified_mentors]

    session.close()
    
    mentors_data= mentor_list

    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    prompt = f""" prompt:
    You need to act like as recommendataion engine for mentor recommendation for student based on below details also the list of mentors with their experience is attached.
    Degree: {user_degree}
    Stream: {user_stream}
    courses opted:{courses}
    Mentor list= {mentors_data}
    Based on above details recommend the mentor that realtes to above details 
    Note: Output should be list in below format:
    [mentor1,mentor2,mentor3,...]
    """
    formatted_prompt = format_prompt(prompt)

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False)
    return jsonify({"ans": stream})


@app.route('/get_streams', methods=['GET'])
def get_streams():
    temperature = 0.9
    max_new_tokens = 256
    top_p = 0.95
    repetition_penalty = 1.0


    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )
    prompt = f""" prompt: 
    You need to act like as recommendation engine.
    List all 40+ streams/branches in like computer science, chemical engineering, aerospace , etc
    Note: Output should be list in below format:
    [branch1, branch2, branch3,...]
    Return only answer not prompt and unnecessary stuff, also dont add any special characters or punctuation marks
    """
    formatted_prompt = format_prompt(prompt)

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False)
    return jsonify({"ans": stream})



@app.route('/get_education_profiles', methods=['GET'])
def get_education_profiles():
    temperature = 0.9
    max_new_tokens = 256
    top_p = 0.95
    repetition_penalty = 1.0

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    sectors = ["engineering", "medical", "arts", "commerce", "science", "management"]  # Example sectors
    prompt = f"""prompt:
    You need to act like a recommendation engine.
    List all education-related profiles in sectors like {', '.join(sectors)}.
    Note: Output should be a list in the below format:
    [profile1, profile2, profile3,...]
    Return only the answer, not the prompt or unnecessary stuff, and don't add any special characters or punctuation marks.
    """

    formatted_prompt = format_prompt(prompt)

    education_profiles = client.text_generation(formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False)
    return jsonify({"ans": education_profiles})

    
if __name__ == '__main__':
    app.run(debug=True)