new code

app.py

@@ -1,14 +1,14 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
-import os
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-# load model
-model_path = os.path.join(os.getcwd(), "review_analysis/output")
-model = AutoModelForSequenceClassification.from_pretrained(model_path, local_files_only=True)
-tokenizer = AutoTokenizer.from_pretrained(model_path, local_files_only=True)
+# Define the local model path
+model_path = "./review_analysis/output"
 
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_path)
 
+# Load the model directly from the local path
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
 
 # Define the inference function
 def predict_sentiment(text):
@@ -19,10 +19,9 @@ def predict_sentiment(text):
     return label_map[predicted_label]
 
 # Gradio interface set-up
+import gradio as gr
 title = "Movie Review Sentiment Analysis"
-description = ("Enter a movie review and find out whether it's Positive or Negative! "
-               "The fine-tuned distilbert-base-uncased model trained on the imdb dataset will try to classify your review.\n\n"
-               "Below are some examples you can try!")
+description = ("Enter a movie review and find out whether it's Positive or Negative!")
 review = gr.Textbox(lines=10, label="Enter your movie review here...")
 prediction = gr.Textbox(label="Sentiment Label (Prediction)")
 examples = [
@@ -31,15 +30,11 @@ examples = [
     ["Absolutely loved it! One of the best movies of all time"]
 ]
 
-# Create the Gradio interface
-intf = gr.Interface(
-    fn=predict_sentiment,
-    title=title,
-    description=description,
-    inputs=review,
-    outputs=prediction,
-    examples=examples
-)
+intf = gr.Interface(fn=predict_sentiment,
+                   title=title,
+                   description=description,
+                   inputs=review,
+                   outputs=prediction,
+                   examples=examples)
 
-# Launch the interface
-intf.launch(inline=False)
+intf.launch(inline=False)