Update app.py

app.py

@@ -1,4 +1,9 @@
 import os
+import gradio as gr
+import numpy as np
+from transformers import AutoTokenizer, AutoModel
+from scipy.special import softmax
+
 import gradio as gr
 import numpy as np
 import pandas as pd
@@ -7,21 +12,22 @@ import transformers
 from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassification,TFAutoModelForSequenceClassification, pipeline
 from scipy.special import softmax
 from dotenv import load_dotenv, dotenv_values
-# from huggingface_hub import login
-
 from huggingface_hub import login
-
-# notebook_login()
 load_dotenv()
 login(os.getenv("access_token"))
+
+
 # Requirements
+# huggingface_token = ""  # Replace with your actual token
 model_path = "imalexianne/distilbert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
 
-tokenizer = AutoTokenizer.from_pretrained(model_path, revision="main")
-config = AutoConfig.from_pretrained(model_path, revision="main")
-model = AutoModelForSequenceClassification.from_pretrained(model_path, revision="main")
+# tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+# tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased", revision="main")
+config = AutoConfig.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
 
-# Preprocess text (username and link placeholders)
+# Preprocessessing function
 def preprocess(text):
     new_text = []
     for x in text.split(" "):
@@ -38,22 +44,22 @@ def sentiment_analysis(text):
     output = model(**encoded_input)
     scores_ = output[0][0].detach().numpy()
     scores_ = softmax(scores_)
-
+    
     # Format output dict of scores
     labels = ["Negative", "Neutral", "Positive"]
     scores = {l:float(s) for (l,s) in zip(labels, scores_) }
-
+    
     return scores
 
 
 # ---- Gradio app interface
 app = gr.Interface(fn = sentiment_analysis,
-                   inputs = gr.Textbox("Write here"),
+                   inputs = gr.Textbox("Write your text here..."),
                    outputs = "label",
                    title = "Sentiment Analysis of Tweets on COVID-19 Vaccines",
                    description  = "Sentiment Analysis of text based on tweets about COVID-19 Vaccines using a fine-tuned 'distilbert-base-uncased' model",
-
+                  
                    examples = [["Covid vaccination has no positive impact"]]
                    )
 
-app.launch(share=True)
+app.launch()