Update app.py

app.py

@@ -1,15 +1,73 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import BertModel, BertConfig
+import torch.nn as nn
+import torch.nn.functional as F
+import huggingface_hub
+from huggingface_hub import hf_hub_download
+
+huggingface_hub.Repository = 'zArabi/Persian-Sentiment-Analysis'
+
+class SentimentModel(nn.Module):
+    def __init__(self, config):
+        super(SentimentModel, self).__init__()
+        self.bert = BertModel.from_pretrained(modelName, return_dict=False)
+        self.dropout = nn.Dropout(0.3)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+    
+    def forward(self, input_ids, attention_mask):
+        _, pooled_output = self.bert(
+            input_ids=input_ids, 
+            attention_mask=attention_mask)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        return logits
+
+modelName = 'HooshvareLab/bert-fa-base-uncased'
+class_names = ['negative', 'neutral', 'positive']
+label2id = {label: i for i, label in enumerate(class_names)}
+id2label = {v: k for k, v in label2id.items()}
+
+config = BertConfig.from_pretrained(
+    modelName,
+    num_labels=len(class_names),
+    id2label=id2label,
+    label2id=label2id)
+
+downloadedModelFile = hf_hub_download(repo_id="zArabi/Persian-Sentiment-Analysis", filename="persianModel")
+loaded_model = torch.load(downloadedModelFile)
+
+max_len=512
 
 pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-def predict(image):
-  predictions = pipeline(image)
-  return {p["label"]: p["score"] for p in predictions}
+def predict(text):
+  text = cleaning(text)
+  encoding = tokenizer.encode_plus(
+    sample_text,
+    max_length=max_len,
+    truncation=True,
+    padding="max_length",
+    add_special_tokens=True, # Add '[CLS]' and '[SEP]'
+    return_token_type_ids=True,
+    return_attention_mask=True,
+    return_tensors='pt',  # Return PyTorch tensors
+  )
+  input_ids = encoding["input_ids"].to(device)
+  attention_mask = encoding["attention_mask"].to(device)
+  outputs = loaded_model (input_ids, attention_mask)
+  probs = F.softmax(outputs,dim=1)
+  values, indices = torch.max(probs, dim=1)
+  data = {
+    'comments': sample_text,
+    'preds': indices.cpu().numpy()[0],
+    'label': class_names[indices.cpu().numpy()[0]],
+    'probablities': {class_names[i] : round(probs[0][i].item(),3) for i in range(len(probs[0]))}
+  }
+  return data
 
 gr.Interface(
     predict,
-    inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
-    outputs=gr.outputs.Label(num_top_classes=2),
-    title="Hot Dog? Or Not?",
+    inputs=gr.Textbox(label="Explore your sentence!",lines=2, placeholder="Type Here..."),
+    outputs=gr.outputs.Label(num_top_classes=3),
+    title="What are feeling?!",
 ).launch()