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from flask import Flask, request, jsonify
device = "cuda" # the device to load the model onto
from ctransformers import AutoModelForCausalLM
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-v0.1-GGUF", model_file="mistral-7b-v0.1.Q4_K_M.gguf", model_type="mistral", gpu_layers=00)
app = Flask(__name__)
@app.route('/')
def home():
return jsonify({"message": "Welcome to the Recommendation API!"})
@app.route('/recommend', methods=['POST'])
def recommendation():
content = request.json
user_degree = content.get('degree')
user_stream = content.get('stream')
user_semester = content.get('semester')
prompt = """
You need to act like as recommendataion engine for course recommendation for student based on below details.
Degree: {user_degree}
Stream: {user_stream}
Current Semester: {user_semester}
Based on above details recommend the courses that realtes to above details
Note: Output should bevalid json format in below format:
{{"course1:ABC,course2:DEF,course3:XYZ,...}}
"""
suffix="[/INST]"
prefix="[INST] <<SYS>> You are a helpful assistant <</SYS>>"
prompt = f"{prefix}{user.replace('{prompt}', prompt)}{suffix}"
return jsonify({"ans":llm(prompt)})
@app.route('/get_mentor', methods=['POST'])
def recommendation():
content = request.json
user_degree = content.get('degree')
user_stream = content.get('stream')
user_semester = content.get('semester')
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}
Based on above details recommend the mentor that realtes to above details
Note: Output should bevalid json format in below format:
{{"mentor1:ABC,mentor2:DEF,mentor3:XYZ,...}}
"""
suffix="[/INST]"
prefix="[INST] <<SYS>> You are a helpful assistant <</SYS>>"
prompt = f"{prefix}{user.replace('{prompt}', prompt)}{suffix}"
return jsonify({"ans":llm(prompt)})
if __name__ == '__main__':
app.run(debug=True) |