Spaces:
Sleeping
Sleeping
File size: 3,444 Bytes
509ca73 423a42f 822dfd5 1af9f6b 509ca73 6d8186b 509ca73 25f7cba 7497699 333978a 10f9ea6 333978a 10f9ea6 1af9f6b 8b77a26 772d03e e60d9fc 10f9ea6 e60d9fc 09a4a4b e60d9fc 09a4a4b 5abdf22 09a4a4b 10f9ea6 e60d9fc ccb7aa0 370c257 24c6700 772d03e 1af9f6b 24c6700 25f7cba 1af9f6b 423a42f c34d039 24c6700 25f7cba 24c6700 5abdf22 24c6700 10f9ea6 423a42f ccb7aa0 370c257 24c6700 509ca73 1af9f6b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
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!"})
import random
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('/get_course', methods=['POST'])
def recommend():
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')
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 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 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 list in below format:
[mentor1,mentor2,mentor3,...]
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})
if __name__ == '__main__':
app.run(debug=True)
|