Update app.py

app.py

@@ -4,51 +4,39 @@ import torch as torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
 
 
-import requests
-from transformers import pipeline
+# import requests
+# from transformers import pipeline
 
-# Load the model using its ID or name
-model_id_or_name = "peace4ever/roberta-large-finetuned-mongolian_v4"  # Update with your model ID or name
-classifier = pipeline("sentiment-analysis", model=model_id_or_name)
+# # Load the model using its ID or name
+# model_id_or_name = "peace4ever/roberta-large-finetuned-mongolian_v4"  # Update with your model ID or name
+# classifier = pipeline("sentiment-analysis", model=model_id_or_name)
 
-# Example usage
-result = classifier("I loved Star Wars so much!")
-print(result)
+# # Example usage
+# result = classifier("I loved Star Wars so much!")
+# print(result)
 
 
-# API_URL = "https://api-inference.huggingface.co/models/peace4ever/roberta-large-finetuned-mongolian_v4"
-# 
+model_name = "peace4ever/roberta-large-finetuned-mongolian_v4"  
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-# def query(payload):
-# 	response = requests.post(API_URL, headers=headers, json=payload)
-# 	return response.json()
-	
-# output = query({
-# 	"inputs": "I like you. I love you",
-# })
+text = st.text_area("Өгүүлбэр оруулна уу?")
+encoded_input = tokenizer(text, return_tensors="pt")
+output = model(**encoded_input)
 
+label_map = {"positive": 0, "negative": 1, "neutral": 2}
 
-# model_name = "peace4ever/roberta-large-finetuned-mongolian_v4"  
-# tokenizer = AutoTokenizer.from_pretrained(model_name)
-# model = AutoModelForSequenceClassification.from_pretrained(model_name)
+# Update the model configuration with custom labels
+config = AutoConfig.from_pretrained(model_name)
+config.label2id = {"positive": 0, "negative": 1, "neutral": 2}
+config.id2label = {0: "positive", 1: "negative", 2: "neutral"}
+config.save_pretrained(model_name)
 
-# text = st.text_area("Өгүүлбэр оруулна уу?")
-# encoded_input = tokenizer(text, return_tensors="pt")
-# output = model(**encoded_input)
+predicted_label_id = torch.argmax(output.logits, dim=1).item()
+id2label = model.config.id2label
+predicted_label = id2label[predicted_label_id]
 
-# label_map = {"positive": 0, "negative": 1, "neutral": 2}
-
-# # Update the model configuration with custom labels
-# config = AutoConfig.from_pretrained(model_name)
-# config.label2id = {"positive": 0, "negative": 1, "neutral": 2}
-# config.id2label = {0: "positive", 1: "negative", 2: "neutral"}
-# config.save_pretrained(model_name)
-
-# predicted_label_id = torch.argmax(output.logits, dim=1).item()
-# id2label = model.config.id2label
-# predicted_label = id2label[predicted_label_id]
-
-# print("Predicted Class:", predicted_label)
+print("Predicted Class:", predicted_label)
 
 # st.json(predicted_label)