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Create app.py
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app.py
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# prompt: write a gradio app to infer the labels from the model we previously trained
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import gradio as gr
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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# Load the fine-tuned model and tokenizer
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checkpoint = "25b3nk/ollama-issues-classifier" # Replace with the actual path to your checkpoint directory
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model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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# Move the model to GPU if available
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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# Function to perform inference
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def predict(text):
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
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outputs = model(**inputs)
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logits = outputs.logits
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probabilities = torch.sigmoid(logits) # Use sigmoid for multi-label classification
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# Get predicted labels based on a threshold (e.g., 0.5)
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predicted_labels = (probabilities > 0.5).nonzero()[:, 1].tolist()
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# Map label IDs back to label names
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predicted_labels_names = [model.config.id2label[label_id] for label_id in predicted_labels]
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return predicted_labels_names
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# Create the Gradio interface
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iface = gr.Interface(
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fn=predict,
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inputs=gr.Textbox(lines=5, placeholder="Enter the issue text here..."),
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outputs=gr.Label(num_top_classes=len(model.config.id2label)), # Display predicted labels
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title="Issue Label Prediction",
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description="Enter an issue description to predict its labels.",
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)
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iface.launch(debug=True)
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