pushing files to the repo from the example!

README.md

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+---
+license: mit
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-regression
+widget:
+  structuredData:
+    AMBIENT_TEMPERATURE:
+    - 21.4322062
+    - 27.322759933333337
+    - 25.56246340000001
+    DAILY_YIELD:
+    - 0.0
+    - 996.4285714
+    - 685.0
+    DC_POWER:
+    - 0.0
+    - 8358.285714
+    - 6741.285714
+    IRRADIATION:
+    - 0.0
+    - 0.6465474886666664
+    - 0.498367802
+    MODULE_TEMPERATURE:
+    - 19.826896066666663
+    - 45.7407144
+    - 38.252356133333336
+    TOTAL_YIELD:
+    - 7218223.0
+    - 6366043.429
+    - 6372656.0
+---
+
+# Model description
+
+This is a LinearRegression model trained on Solar Power Generation Data.
+
+## Intended uses & limitations
+
+This model is not ready to be used in production.
+
+## Training Procedure
+
+### Hyperparameters
+
+The model is trained with below hyperparameters.
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter   | Value      |
+|------------------|------------|
+| alpha            | 1.0        |
+| copy_X           | True       |
+| fit_intercept    | True       |
+| l1_ratio         | 0.5        |
+| max_iter         | 1000       |
+| normalize        | deprecated |
+| positive         | False      |
+| precompute       | False      |
+| random_state     | 0          |
+| selection        | cyclic     |
+| tol              | 0.0001     |
+| warm_start       | False      |
+
+</details>
+
+### Model Plot
+
+The model plot is below.
+
+<style>#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b {color: black;background-color: white;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b pre{padding: 0;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-toggleable {background-color: white;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-estimator:hover {background-color: #d4ebff;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-item {z-index: 1;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-parallel-item:only-child::after {width: 0;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b div.sk-container {display: inline-block;position: relative;}</style><div id="sk-a3a3b863-d5cf-4b57-9e19-e3d8f2db0a0b" class"sk-top-container"><div class="sk-container"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="d20384ee-8f34-4e73-b4a5-b15dfd56af7a" type="checkbox" checked><label class="sk-toggleable__label" for="d20384ee-8f34-4e73-b4a5-b15dfd56af7a">ElasticNet</label><div class="sk-toggleable__content"><pre>ElasticNet(random_state=0)</pre></div></div></div></div></div>
+
+## Evaluation Results
+
+You can find the details about evaluation process and the evaluation results.
+
+
+
+| Metric   |   Value |
+|----------|---------|
+| accuracy | 99.9994 |
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import pickle 
+with open(dtc_pkl_filename, 'rb') as file: 
+    clf = pickle.load(file)
+```
+
+</details>
+
+
+
+
+# Model Card Authors
+
+This model card is written by following authors:
+
+ayyuce demirbas
+
+# Model Card Contact
+
+You can contact the model card authors through following channels:
+[More Information Needed]
+
+# Citation
+
+Below you can find information related to citation.
+
+**BibTeX:**
+```
+bibtex
+@inproceedings{...,year={2022}}
+```

config.json

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+{
+    "sklearn": {
+        "columns": [
+            "DAILY_YIELD",
+            "TOTAL_YIELD",
+            "AMBIENT_TEMPERATURE",
+            "MODULE_TEMPERATURE",
+            "IRRADIATION",
+            "DC_POWER"
+        ],
+        "environment": [
+            "scikit-learn=1.0"
+        ],
+        "example_input": {
+            "AMBIENT_TEMPERATURE": [
+                21.4322062,
+                27.322759933333337,
+                25.56246340000001
+            ],
+            "DAILY_YIELD": [
+                0.0,
+                996.4285714,
+                685.0
+            ],
+            "DC_POWER": [
+                0.0,
+                8358.285714,
+                6741.285714
+            ],
+            "IRRADIATION": [
+                0.0,
+                0.6465474886666664,
+                0.498367802
+            ],
+            "MODULE_TEMPERATURE": [
+                19.826896066666663,
+                45.7407144,
+                38.252356133333336
+            ],
+            "TOTAL_YIELD": [
+                7218223.0,
+                6366043.429,
+                6372656.0
+            ]
+        },
+        "model": {
+            "file": "solar.pkl"
+        },
+        "task": "tabular-regression"
+    }
+}

solar.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:435b149d9761cf5e1f4ecb85c7e9364a49f5602be18918f8377f54c03f5756d5
+size 778