added model and pipeline

iris_model.pickle

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:86a913ce4c1e18ca494ec94906f5b64f00d94365c977219cd85c40bb8479a4fc
+size 1974

pipeline.py

@@ -1,19 +1,14 @@
 from typing import Dict, List, Union
 import os
+import pandas as pd
+import pickle
 
 class PreTrainedPipeline():
     def __init__(self, path=""):
-        # IMPLEMENT_THIS
-        # Preload all the elements you are going to need at inference.
-        # For instance your model, processors, tokenizer that might be needed.
-        # This function is only called once, so do all the heavy processing I/O here"""
-        raise NotImplementedError(
-            "Please implement PreTrainedPipeline __init__ function"
-        )
+        with open('iris_model.pickle', 'rb') as f:
+            self.pipeline = pickle.load(f)
 
-    def __call__(
-        self, inputs: Dict[str, Dict[str, List[Union[str, float]]]]
-    ) -> List[Union[str, float]]:
+    def __call__(self, inputs):
         """
         Args:
             inputs (:obj:`dict`):
@@ -22,7 +17,8 @@ class PreTrainedPipeline():
         Return:
             A :obj:`list` of floats or strings: The classification output for each row.
         """
-        # IMPLEMENT_THIS
-        raise NotImplementedError(
-            "Please implement PreTrainedPipeline __call__ function"
-        )
+        # Convert the input dictionary to a pandas DataFrame
+        data = inputs['data']  # Extracting the 'data' dictionary
+        X = pd.DataFrame(data)  # Converting 'data' dictionary to DataFrame
+
+        return self.pipeline.predict(X)