Add application file

.gitignore

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+.env
+*venv/

app.py

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+import os
+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
+
+from huggingface_hub import login
+
+# notebook_login()
+load_dotenv()
+login(os.getenv("access_token"))
+# Requirements
+model_path = "imalexianne/distilbert-base-uncased"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, revision="main")
+config = AutoConfig.from_pretrained(model_path, revision="main")
+model = AutoModelForSequenceClassification.from_pretrained(model_path, revision="main")
+
+# Preprocess text (username and link placeholders)
+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)
+    return " ".join(new_text)
+
+# ---- Function to process the input and return prediction
+def sentiment_analysis(text):
+    text = preprocess(text)
+
+    encoded_input = tokenizer(text, return_tensors = "pt") # for PyTorch-based models
+    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"),
+                   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)