Update app.py

app.py

@@ -1,3 +1,37 @@
 import gradio as gr
+from transformers import AutoTokenizer, pipeline, AutoModelForSequenceClassification
 
-gr.Interface.load("models/ethanrom/a2").launch()
+# load the tokenizer and model from Hugging Face
+tokenizer = AutoTokenizer.from_pretrained("ethanrom/a2")
+model = AutoModelForSequenceClassification.from_pretrained("ethanrom/a2")
+
+# define the classification labels
+class_labels = ["Negative", "Positive", "No Impact", "Mixed"]
+
+# create the zero-shot classification pipeline
+classifier = pipeline("zero-shot-classification", model=model, tokenizer=tokenizer, device=0)
+
+# define the Gradio interface
+def predict_sentiment(text, model_choice):
+    if model_choice == "bert":
+        # use the default BERT sentiment analysis pipeline
+        sentiment_classifier = pipeline("sentiment-analysis", device=0)
+        result = sentiment_classifier(text)[0]
+        label = result["label"]
+        score = result["score"]
+        return f"{label} ({score:.2f})"
+    else:
+        # use the fine-tuned RoBERTa model for multi-class classification
+        labels = class_labels
+        hypothesis_template = "This text is about {}."
+        result = classifier(text, hypothesis_template=hypothesis_template, multi_class=True, labels=labels)
+        scores = result["scores"]
+        predicted_label = result["labels"][0]
+        return f"{predicted_label} ({scores[0]:.2f})"
+
+# define the Gradio interface inputs and outputs
+inputs = [gr.inputs.Textbox(label="Input Text"), gr.inputs.Radio(["bert", "fine-tuned RoBERTa"], label="Model Choice")]
+outputs = gr.outputs.Textbox(label="Sentiment Prediction")
+
+# create the Gradio interface
+gr.Interface(predict_sentiment, inputs, outputs, title="Sentiment Analysis App").launch()