intial

app.py

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+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoConfig
+import torch
+import numpy as np
+import pandas as pd
+from scipy.special import softmax
+import gradio as gr
+
+# Load the model and tokenizer
+# setup
+model_name = "benmanks/sentiment_analysis_trainer_model"  
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained("benmanks/sentiment_analysis_trainer_model")
+
+# Function
+def preprocess(text):
+    # Preprocess text (username and link placeholders)
+    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)
+
+    # Tokenize the text
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+
+    # Make a prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Get the predicted class probabilities
+    scores = torch.softmax(outputs.logits, dim=1).tolist()[0]
+
+    # Map the scores to labels
+    labels = ['Negative', 'Neutral', 'Positive']
+    scores_dict = {label: score for label, score in zip(labels, scores)}
+
+    return scores_dict
+
+title = "Sentiment Analysis Application\n\n\nThis application assesses if a twitter post relating to vaccination "
+description = "This application assesses if a twitter post relating to vaccination is positive,neutral or negative"
+
+demo = gr.Interface(
+    fn=sentiment_analysis,
+    inputs=gr.Textbox(placeholder="Write your tweet here..."),
+    outputs=gr.Label(num_top_classes=3),
+    examples=[["The Vaccine is harmful!"],["I cant believe people don't vaccinate their kids"],["FDA think just not worth the AE unfortunately"],["For a vaccine given to healthy"]],
+    title=title,
+    description=description
+    
+)
+
+demo.launch(share=True)

requirements.txt

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+transformers
+gradio
+numpy
+scipy
+pandas
+torch