first commit

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
+
+#model_path=f'sotseth/output'
+model_path=f'https://huggingface.co/sotseth/output'
+
+model=AutoModelForSequenceClassification.from_pretrained(model_path)
+#tokenizer=AutoTokenizer.from_pretrained(f"cardiffnlp/twitter-roberta-base-sentiment-latest")
+tokenizer=AutoTokenizer.from_pretrained(model_path)
+
+#from huggingface_hub import notebook_login
+#notebook_login()
+
+#tokenizer.push_to_hub('sotseth/output')
+
+def predict_tweet(tweet):
+
+    inputs = tokenizer(tweet, return_tensors="pt", padding=True)
+    outputs = model(**inputs)
+    probs = outputs.logits.softmax(dim=-1)
+    sentiment_classes = ['Negative', 'Neutral', 'Positive']
+
+    return {sentiment_classes[i]: float(probs[0, i]) for i in range(len(sentiment_classes))}
+
+iface=gr.Interface(
+    fn=predict_tweet,
+    inputs="text",
+    outputs="label",
+    title="Vaccine Sentiment Classifier",
+    description="Enter your thought on vaccines",
+    examples=[
+        ["Vaccines are a game-changer in addressing public health"],
+        ["Vaccines are profit making"],
+        ["Vaccines are dangerous"]
+    ]
+
+)
+
+iface.launch(share=True)
+

requirements.txt

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+transformers==4.35.0
+gradio==4.2.0
+scikit-learn==1.2.2
+scipy==1.11.3
+torch==2.1.0
+
+