Create run.py
Browse files
run.py
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import json
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import os
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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# Load the model from Hugging Face
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model_path = "your_model_path" # Replace with your own model path
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tokenizer = AutoTokenizer.from_pretrained("OttoYu/Tree-Dbh")
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model = AutoModelForSequenceClassification.from_pretrained("OttoYu/Tree-Dbh")
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# Set up the inference pipeline
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text_classification = pipeline(
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"text-classification",
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model=model,
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tokenizer=tokenizer,
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device=0 if torch.cuda.is_available() else -1,
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return_all_scores=True,
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)
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# Define a function to get the predicted tree height and crown spread for a given dbh
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def predict_tree_properties(dbh):
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# Prepare the input text
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input_text = f"dbh: {dbh}"
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# Get the predicted probabilities for each class
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results = text_classification(input_text)
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probs = results[0]["scores"]
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# Convert the probabilities to tree height and crown spread
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tree_height = probs[0] * 100 # Scale the probability to 0-100
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crown_spread = probs[1] * 10 # Scale the probability to 0-10
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# Return the predicted tree properties
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return {"tree_height": tree_height, "crown_spread": crown_spread}
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# Define a function to get user input and display the predicted tree properties
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def run_inference():
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# Get user input for dbh
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dbh = input("Enter the dbh value (in cm): ")
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# Make the prediction and display the results
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tree_properties = predict_tree_properties(dbh)
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print(f"Predicted Tree Height: {tree_properties['tree_height']:.2f} m")
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print(f"Predicted Crown Spread: {tree_properties['crown_spread']:.2f} m")
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# Call the function to run the inference
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run_inference()
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