Update app.py file

Update app.py file to remove the local_files_only=True parameter from from_pretrained() calls.

app.py

@@ -1,55 +1,56 @@
-import gradio as gr
-import torch
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-# Defining the model path relative to the repository root.
-model_path = "abdullah123456/NLP_Project"
-
-# Loading the model and tokenizer from the local directory.
-# The parameter `local_files_only=True` ensures that the files are loaded from the repository.
-model = AutoModelForSequenceClassification.from_pretrained(model_path, local_files_only=True)
-tokenizer = AutoTokenizer.from_pretrained(model_path, local_files_only=True)
-
-# Defining a simple text cleaning function
-def clean_text(text):
-    return " ".join(text.split())
-
-# Defining the prediction function that the web interface will use.
-def predict_sentiment(tweet: str) -> str:
-
-    # Cleaning the tweet
-    tweet_clean = clean_text(tweet)
-
-    # Tokenizing the tweet. 
-    inputs = tokenizer(tweet_clean, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
-
-    # Moving the input tensors to the same device as the model.
-    inputs = {k: v.to(model.device) for k, v in inputs.items()}
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-    # Getting the predicted class index.
-    predicted_class = torch.argmax(outputs.logits, dim=1).item()
-    
-    # Defining label mapping.
-    label_mapping = {0: "negative", 1: "neutral", 2: "positive"}
-    return label_mapping.get(predicted_class, "unknown")
-
-# Creating the Gradio Interface.
-iface = gr.Interface(
-    fn=predict_sentiment,
-    inputs=gr.Textbox(lines=4, placeholder="Enter an Urdu tweet here...", label="Urdu Tweet"),
-    outputs=gr.Textbox(label="Predicted Sentiment"),
-    title="Urdu Tweet Sentiment Analysis",
-    description="This app uses a fine-tuned transformer model to predict the sentiment of Urdu tweets. "
-                "Enter your tweet in the textbox below and click 'Submit' to see the prediction.",
-    examples=[
-        ["السلام علیکم! آج کا دن بہت خوبصورت ہے۔"],
-        ["میں بہت غمگین ہوں، دل بہت دکھ رہا ہے۔"],
-        ["آپ کا کام بہت اچھا ہے!"]
-    ]
-)
-
-# Launching the interface.
-if __name__ == "__main__":
+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Defining the model path relative to the repository root.
+model_path = "abdullah123456/NLP_Project"
+
+# Loading the model and tokenizer from the local directory.
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+
+# Defining a simple text cleaning function
+def clean_text(text):
+    return " ".join(text.split())
+    
+
+# Defining the prediction function that the web interface will use.
+def predict_sentiment(tweet: str) -> str:
+
+    # Cleaning the tweet
+    tweet_clean = clean_text(tweet)
+
+    # Tokenizing the tweet. 
+    inputs = tokenizer(tweet_clean, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
+
+    # Moving the input tensors to the same device as the model.
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+    # Getting the predicted class index.
+    predicted_class = torch.argmax(outputs.logits, dim=1).item()
+    
+    # Defining label mapping.
+    label_mapping = {0: "negative", 1: "neutral", 2: "positive"}
+    return label_mapping.get(predicted_class, "unknown")
+
+# Creating the Gradio Interface.
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=4, placeholder="Enter an Urdu tweet here...", label="Urdu Tweet"),
+    outputs=gr.Textbox(label="Predicted Sentiment"),
+    title="Urdu Tweet Sentiment Analysis",
+    description="This app uses a fine-tuned transformer model to predict the sentiment of Urdu tweets. "
+                "Enter your tweet in the textbox below and click 'Submit' to see the prediction.",
+    examples=[
+        ["السلام علیکم! آج کا دن بہت خوبصورت ہے۔"],
+        ["میں بہت غمگین ہوں، دل بہت دکھ رہا ہے۔"],
+        ["آپ کا کام بہت اچھا ہے!"]
+    ]
+)
+
+# Launching the interface.
+if __name__ == "__main__":
     iface.launch()