Upload the requirements file and app.py file

app.py

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+# Necessary imports
+import gradio as gr
+from transformers import pipeline
+
+# Load the zero-shot classification model
+classifier = pipeline(
+    "zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v2.0"
+)
+
+
+# Function to perform zero-shot classification
+def ZeroShotTextClassification(text_input, candidate_labels):
+    """
+    Performs zero-shot classification on the given text input using the provided candidate labels.
+
+    Args:
+      text_input (str): The input text to classify.
+      candidate_labels (str): A comma-separated string of candidate labels.
+
+    Returns:
+      dict: A dictionary containing the predicted labels as keys and their corresponding scores as values.
+    """
+
+    # Split the candidate labels
+    labels = [label.strip(" ") for label in candidate_labels.split(",")]
+
+    # Output dictionary to store the predicted labels and their scores
+    output = {}
+
+    # Perform zero-shot classification
+    prediction = classifier(text_input, labels)
+
+    # Create a dictionary with the predicted labels and their corresponding scores
+    for i in range(len(prediction["labels"])):
+        output[prediction["labels"][i]] = prediction["scores"][i]
+
+    # Return the output
+    return output
+
+
+# Examples to display in the interface
+examples = [
+    ["I love to play the guitar", "music, artist, food, travel"],
+    ["I am a software engineer at Google", "technology, engineering, art, science"],
+    ["I am a professional basketball player", "sports, athlete, chef, politics"],
+]
+
+# Launch the interface
+demo = gr.Interface(
+    fn=ZeroShotTextClassification,
+    inputs=[gr.Textbox(label="Input"), gr.Textbox(label="Candidate Labels")],
+    outputs=gr.Label(label="Classification"),
+    title="Zero Shot Text Classification",
+    description="Classify text using zero-shot classification with DeBERTa-v3-large-zeroshot model! Provide a text input and a list of candidate labels separated by commas.",
+    examples=examples,
+    theme="Soft",
+    allow_flagging="never",
+)
+demo.launch()

requirements.txt

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+gradio
+torch
+timm
+sentencepiece
+transformers