Update selection.py

selection.py

@@ -1,5 +1,6 @@
+import requests
 import os
-import numpy as np
+
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 from datasets import load_dataset
@@ -33,19 +34,16 @@ most_relevant_context = find_most_relevant_context(contexts, Question)
 # Create the instruction for the model
 instruction = most_relevant_context[:300] + " " + Question
 
-# Load Mistral model and tokenizer
-model_name = "mistralai/Mistral-7B-v0.1"  # Replace with the appropriate Mistral model name if available
-tokenizer = AutoTokenizer.from_pretrained(model_name , token= my_token)
-model = AutoModelForCausalLM.from_pretrained(model_name, token= my_token)
-
-
-# Tokenize the input
-input_ids = tokenizer.encode(instruction, return_tensors='pt')
 
-# Generate response
-outputs = model.generate(input_ids, max_new_tokens=150, num_return_sequences=1)
 
-# Decode the response
-response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(response)
+API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
+headers = {"Authorization": f"Bearer {my_token}"}
 
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json()
+	
+output = query({
+	"inputs": instruction,
+})
+print(output)