Create run.py

run.py

@@ -0,0 +1,47 @@
+import json
+import os
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+# Load the model from Hugging Face
+model_path = "your_model_path"  # Replace with your own model path
+tokenizer = AutoTokenizer.from_pretrained("OttoYu/Tree-Dbh")
+model = AutoModelForSequenceClassification.from_pretrained("OttoYu/Tree-Dbh")
+
+# Set up the inference pipeline
+text_classification = pipeline(
+    "text-classification",
+    model=model,
+    tokenizer=tokenizer,
+    device=0 if torch.cuda.is_available() else -1,
+    return_all_scores=True,
+)
+
+# Define a function to get the predicted tree height and crown spread for a given dbh
+def predict_tree_properties(dbh):
+    # Prepare the input text
+    input_text = f"dbh: {dbh}"
+    
+    # Get the predicted probabilities for each class
+    results = text_classification(input_text)
+    probs = results[0]["scores"]
+    
+    # Convert the probabilities to tree height and crown spread
+    tree_height = probs[0] * 100  # Scale the probability to 0-100
+    crown_spread = probs[1] * 10  # Scale the probability to 0-10
+    
+    # Return the predicted tree properties
+    return {"tree_height": tree_height, "crown_spread": crown_spread}
+
+# Define a function to get user input and display the predicted tree properties
+def run_inference():
+    # Get user input for dbh
+    dbh = input("Enter the dbh value (in cm): ")
+    
+    # Make the prediction and display the results
+    tree_properties = predict_tree_properties(dbh)
+    print(f"Predicted Tree Height: {tree_properties['tree_height']:.2f} m")
+    print(f"Predicted Crown Spread: {tree_properties['crown_spread']:.2f} m")
+
+# Call the function to run the inference
+run_inference()