Create app.py

app.py

@@ -0,0 +1,48 @@
+from flask import Flask, jsonify, request
+from transformers import pipeline, AutoTokenizer
+import joblib
+import json
+
+# Load the model
+model = joblib.load("iris_svm.joblib")
+
+# Load the configuration file
+with open("config.json", "r") as f:
+    config = json.load(f)
+
+# Get the input features and target variable
+features = config["features"]
+target = config["targets"][0]
+target_mapping = config["target_mapping"]
+
+# Initialize the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+
+# Initialize the Flask app
+app = Flask(__name__)
+
+# Define the prediction route
+@app.route("/predict", methods=["POST"])
+def predict():
+    # Get the input data
+    input_data = request.json
+
+    # Construct the input text for the pipeline
+    input_text = f"SepalLengthCm: {input_data['SepalLengthCm']}, SepalWidthCm: {input_data['SepalWidthCm']}, PetalLengthCm: {input_data['PetalLengthCm']}, PetalWidthCm: {input_data['PetalWidthCm']}"
+
+    # Tokenize the input text
+    tokenized_input = tokenizer(input_text, return_tensors="pt")
+
+    # Make a prediction using the pipeline
+    classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0)
+    predicted_class_id = classifier(tokenized_input)[0]['label']
+
+    # Convert the predicted class ID to a class name
+    predicted_class_name = list(target_mapping.keys())[list(target_mapping.values()).index(predicted_class_id)]
+
+    # Return the predicted class name as a JSON response
+    return jsonify({"predicted_class": predicted_class_name})
+
+# Run the Flask app
+if __name__ == "__main__":
+    app.run(debug=True)