Update app.py

app.py

@@ -3,27 +3,52 @@ import streamlit as st
 import torch as torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
 
-model_name = "peace4ever/roberta-large-finetuned-mongolian_v4"  
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-text = st.text_area("Өгүүлбэр оруулна уу?")
-encoded_input = tokenizer(text, return_tensors="pt")
-output = model(**encoded_input)
+import requests
+from transformers import pipeline
 
-label_map = {"positive": 0, "negative": 1, "neutral": 2}
+# 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)
 
-# 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)
+# Example usage
+result = classifier("I loved Star Wars so much!")
+print(result)
 
-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)
+# API_URL = "https://api-inference.huggingface.co/models/peace4ever/roberta-large-finetuned-mongolian_v4"
+# 
+
+# def query(payload):
+# 	response = requests.post(API_URL, headers=headers, json=payload)
+# 	return response.json()
+	
+# output = query({
+# 	"inputs": "I like you. I love you",
+# })
+
+
+# model_name = "peace4ever/roberta-large-finetuned-mongolian_v4"  
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
+# model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# 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}
+
+# # 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)
 
 # st.json(predicted_label)