Update app.py

app.py

@@ -1,42 +1,51 @@
+# import streamlit as st
+
+# import torch as torch
+# from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
+
+
 import streamlit as st
+from transformers import pipeline
+from PIL import Image
 
-import torch as torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
+st.title("Hot Dog? Or Not?")
 
-# import requests
-# from transformers import pipeline
+file_name = st.file_uploader("Upload a hot dog candidate image")
 
-# # 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)
+if file_name is not None:
+    col1, col2 = st.columns(2)
 
-# # Example usage
-# result = classifier("I loved Star Wars so much!")
-# print(result)
+    image = Image.open(file_name)
+    col1.image(image, use_column_width=True)
+    predictions = pipeline(image)
 
+    col2.header("Probabilities")
+    for p in predictions:
+        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
 
-model_name = "peace4ever/roberta-large-finetuned-mongolian_v3"  
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
+# model_name = "peace4ever/roberta-large-finetuned-mongolian_v3"  
+# 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)
+# 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}
+# 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)
+# # 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]
+# 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)