Add application file

app.py

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+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoConfig
+import numpy as np
+import gradio as gr
+
+
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Specifying the model path, which points to the Hugging Face Model Hub
+model_path = f'Mbabazi/twitter-roberta-base-sentiment-latest'
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+
+# Function to predict sentiment of a given tweet
+def predict_tweet(tweet):
+    # Tokenize the input tweet using the specified tokenizer
+    inputs = tokenizer(tweet, return_tensors="pt", padding=True, truncation=True, max_length=128)
+    
+    # Passing the tokenized input through the pre-trained sentiment analysis model
+    outputs = model(**inputs)
+    
+    # Applying softmax to obtain probabilities for each sentiment class
+    probs = outputs.logits.softmax(dim=-1)
+    
+    # Defining sentiment classes
+    sentiment_classes = ['Negative', 'Neutral', 'Positive']
+    
+    # Creating a dictionary with sentiment classes as keys and their corresponding probabilities as values
+    return {sentiment_classes[i]: float(probs.squeeze()[i]) for i in range(len(sentiment_classes))}
+
+
+# Create a Gradio Interface for the tweet sentiment prediction function
+iface = gr.Interface(
+    fn=predict_tweet,  # Set the prediction function
+    inputs="text",  # Specify input type as text
+    outputs="label",  # Specify output type as label
+    title="Tweet Sentiment Classifier",  # Set the title of the interface
+    description="Enter a tweet to determine if the sentiment is negative, neutral, or positive."  # Provide a brief description
+)
+
+iface.launch()