App init

App.py

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+# Importing the library
+
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+from huggingface_hub import notebook_login
+
+
+
+# Load the Pre-trained Model and Tokenizer:
+
+model_name = "Shiko07/tuned_test_trainer-bert-base-uncased"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+#Defining the Gradio Interface:
+
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    predicted_class = torch.argmax(outputs.logits, dim=1).item()
+    return {0: "Negative", 1: "Neutral", 2: "Positive"}[predicted_class]
+
+#Creating Gradio interface:
+
+custom_css = """
+    .gradio {
+        background-color: #0074D9;  /* Change background color to blue */
+    }
+"""
+# predict_sentiment function
+
+
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=3, label="Enter your text:"),
+    outputs="text",
+    # theme="custom_css",
+    title="Marrakech Sentiment Analysis App",
+    description="An app for sentiment analysis for Tweet posts on covid 19 vaccine.",
+    css=custom_css,
+    examples = [ ["Vaccine misinformation is harmful."],
+                 ["I'm hopeful about the vaccine."],
+                 ["Second dose excitement."],
+                 ["I'm worried about vaccine side effects."],
+                 ["Vaccine distribution updates are available."],
+                 ["Vaccine distribution is too slow."],
+                 ["I'm gathering information about the vaccine."]
+    ]
+
+
+)
+
+interface.launch()

requirements.txt

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+gradio
+transformers
+numpy
+scipy