Update app.py

app.py

@@ -1,32 +1,42 @@
 import os
 import gradio as gr
+import numpy as np
 from transformers import AutoTokenizer, AutoModel
 from scipy.special import softmax
-from huggingface_hub import login
 
-# Load environment variables
-from dotenv import load_dotenv
+import gradio as gr
+import numpy as np
+import pandas as pd
+import pickle
+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
 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)
 
-model = AutoModel.from_pretrained(model_path)
+# 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)
 
-# Preprocessing function
+# Preprocessessing function
 def preprocess(text):
     new_text = []
-    for x in text.split(" "):
-        x = "@user" if x.startswith("@") and len(x) > 1 else x
-        x = "http" if x.startswith("http") else x
-        new_text.append(x)
+    for t in text.split(" "):
+        t = "@user" if t.startswith("@") and len(t) > 1 else t
+        t = "http" if t.startswith("http") else t
+        new_text.append(t)
     return " ".join(new_text)
 
-# Function to process the input and return prediction
+# ---- Function to process the input and return prediction
 def sentiment_analysis(text):
     text = preprocess(text)
 
@@ -41,14 +51,15 @@ def sentiment_analysis(text):
     
     return scores
 
-# Gradio app interface
-app = gr.Interface(
-    fn=sentiment_analysis,
-    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()
+
+# ---- Gradio app interface
+app = gr.Interface(fn = sentiment_analysis,
+                   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()