Update app.py

app.py

@@ -1,32 +1,44 @@
 import numpy as np
 import os
 import gradio as gr
+import torch
+from PIL import image
 
 os.environ["WANDB_DISABLED"] = "true"
 
 from datasets import load_dataset, load_metric
 from transformers import (
     AutoConfig,
-    # AutoModelForSequenceClassification,
+    AutoModelForSequenceClassification,
     AutoTokenizer,
     TrainingArguments,
     logging,
 pipeline
 )
 
+id2label = {0: "negative", 1: "neutral", 2: "positive"}
+label2id = {"negative": 0, "neutral": 1, "positive": 2}
+
+model = AutoModelForSequenceClassification.from_pretrained(
+        model="FFZG-cleopatra/M2SA",
+        num_labels=3, id2label=id2label,
+        label2id=label2id
+    )
 
-analyzer = pipeline(
-    "sentiment-analysis", model="FFZG-cleopatra/M2SA"
-)
 
-def predict_sentiment(x):
-    print(analyzer(x))
-    return "test" #|label2id[analyzer(x)[0]["label"]]
+def predict_sentiment(text, image):
+    print(text, image)
+    prediction = None
+    with torch.no_grad():
+        model(x)
+        print(analyzer(x))
+        
+    return prediction
 
 
 interface = gr.Interface(
-    fn=predict_sentiment,
-    inputs='text',
+    fn=lambda text, image: predict_sentiment(text, image),,
+    inputs=[gr.inputs.Textbox(),gr.inputs.Image(shape=(224, 224))]
     outputs=['text'],
     title='Multilingual-Multimodal-Sentiment-Analysis',
     examples= ["I love tea","I hate coffee"],