App script

app.py

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+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoConfig
+import numpy as np
+from scipy.special import softmax
+import gradio as gr
+
+# Requirements
+model_path = f"FKBaffour/fine-tuned_bert_based_model_for_sentiment_analysis"
+tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
+config = AutoConfig.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+# Preprocess text (username and link placeholders)
+def preprocess(text):
+    new_text = []
+    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)
+
+
+def sentiment_analysis(text):
+    text = preprocess(text)
+
+    # PyTorch-based models
+    encoded_input = tokenizer(text, return_tensors='pt')
+    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
+
+demo = gr.Interface(
+    fn=sentiment_analysis, 
+    inputs=gr.Textbox(placeholder="Write your tweet here..."), 
+    outputs="label", 
+    interpretation="default",
+    examples=[["It is very bad to vaccinate your child!"]])
+
+demo.launch()
+