main.py

from flask import Flask, request, jsonify
from huggingface_hub import InferenceClient


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

app = Flask(__name__)

file_path = "mentor.txt"
with open(file_path, "r") as file:
    mentors_data = file.read()

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


def format_prompt(message):
    prompt = "<s>"
    prompt += f"[INST] {message} [/INST]"
    prompt += "</s>"
    return prompt

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

    if request.method == 'POST':
        content = request.json
        user_degree = content.get('degree')
        user_stream = content.get('stream')
        user_semester = content.get('semester')

        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 based on below details.
        Degree: {user_degree}
        Stream: {user_stream}
        Current Semester: {user_semester}
        Based on above details recommend the courses that relate to the above details 
        Note: Output should be valid json format in below format:
        {{"course1:course_name, course2:course_name, course3:course_name,...}}
        """
        formatted_prompt = format_prompt(prompt)

        stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
        output = ""

        for response in stream:
            output += response.token.text
            return jsonify({"ans": output})
    else:
        return jsonify({"error": "Invalid request method"})

@app.route('/get_mentor', methods=['POST'])
def mentor():
    temperature=0.9
    max_new_tokens=256
    top_p=0.95
    repetition_penalty=1.0
    content = request.json
    user_degree = content.get('degree')
    user_stream = content.get('stream')
    user_semester = content.get('semester')
    courses = content.get('courses')
    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}
    Current Semester: {user_semester}
    courses opted:{courses}

    Mentor list= {mentors_data}
    Based on above details recommend the mentor that realtes to above details 
    Note: Output should be valid json format in below format:
    {{"mentor1:mentor_name,mentor2:mentor_name,mentor3:mentor_name,...}}
    """
    formatted_prompt = format_prompt(prompt)

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text
        yield output
    return jsonify({"ans":output})


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