Cargando Model

app.py

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+import gradio as gr
+import pandas as pd
+import pickle
+
+
+# Define params names
+PARAMS_NAME = [
+            "ph",
+            "Hardness",
+            "Solids",
+            "Chloramines",
+            "Sulfate",
+            "Conductivity",
+            "Organic_carbon",
+            "Trihalomethanes",
+            "Turbidity"
+]
+
+           
+# Load model
+with open("model/model1.pkl", "rb") as f:
+    model = pickle.load(f)
+
+
+
+
+def predict(*args):
+    answer_dict = {}
+
+    for i in range(len(PARAMS_NAME)):
+        answer_dict[PARAMS_NAME[i]] = [args[i]]
+
+    # Crear dataframe
+    single_instance = pd.DataFrame.from_dict(answer_dict)
+
+ 
+    
+    
+    prediction = model.predict(single_instance)
+
+    # Como sabemos el model nos devuelve los tipos de fraude 1, 2 y 3 en el response. Podemos devolver un response estilo semáforo.
+    
+    # Cast numpy.int64 to just a int
+    Potability = int(prediction[0])
+
+
+    # Adaptación respuesta
+    response = Potability
+    if Potability == 1:
+        response = "Just Water, drink it 🥤"
+    if Potability == 0:
+        response = "Drink and you will DIE! ☠️ "
+
+
+    return response
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        #  🏞️ Water Quality 
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+
+            gr.Markdown(
+                """
+                ## 🌊 Insert your water data here please 💦
+                """
+            )
+            
+            ph = gr.Slider(
+                label='Ph',
+                minimum=0.2274990502021987,
+                maximum=13.999999999999998,
+                step=0.1,
+                randomize=True
+            )
+
+            Hardness = gr.Slider(
+                label='Hardness',
+                minimum=73.4922336890611,
+                maximum=317.33812405558257,
+                step=0.1,
+                randomize=True
+            )
+
+            Solids = gr.Slider(
+                label='Solids',
+                minimum=320.942611274359,
+                maximum=56488.67241273919,
+                step=0.1,
+                randomize=True
+            )
+
+            Chloramines = gr.Slider(
+                label='Chloramines',
+                minimum=1.3908709048851806,
+                maximum=13.127000000000002,
+                step=0.1,
+                randomize=True
+            )
+
+            Sulfate = gr.Slider(
+                label='Sulfate',
+                minimum=129.00000000000003,
+                maximum=481.0306423059972,
+                step=0.1,
+                randomize=True
+            )
+
+            Conductivity = gr.Slider(
+                label='Conductivity',
+                minimum=201.6197367551575,
+                maximum=753.3426195583046,
+                step=0.1,
+                randomize=True
+            )
+
+            Organic_carbon = gr.Slider(
+                label='Organic_Carbon',
+                minimum=2.1999999999999886,
+                maximum=27.00670661116601,
+                step=0.1,
+                randomize=True
+            )
+
+            Trihalomethanes = gr.Slider(
+                label='Trihalomethanes',
+                minimum=8.577012932983806,
+                maximum=124.0,
+                step=0.1,
+                randomize=True
+            )
+
+            Turbidity = gr.Slider(
+                label='Turbidity',
+                minimum=1.45,
+                maximum=6.494748555990993,
+                step=0.1,
+                randomize=True
+            )
+
+
+        with gr.Column():
+
+            gr.Markdown(
+                """
+                ## Let's see if you can drink it 💧
+                """
+            )
+
+            label = gr.Label(label="Tipo de Fraude")
+            predict_btn = gr.Button(value="Evaluar")
+            predict_btn.click(
+                predict,
+                inputs=[
+                    ph,
+                    Hardness,
+                    Solids,
+                    Chloramines,
+                    Sulfate,
+                    Conductivity,
+                    Organic_carbon,
+                    Trihalomethanes,
+                    Turbidity,
+                ],
+                outputs=[label],
+                api_name="prediccion"
+            )
+    gr.Markdown(
+        """
+        <p style='text-align: center'>
+            <a href='https://www.escueladedatosvivos.ai/cursos/bootcamp-de-data-science' 
+                target='_blank'>Proyecto demo creado en el bootcamp de EDVAI 🤗
+            </a>
+        </p>
+        <p style='text-align: center'>
+            <a href='https://www.kaggle.com/datasets/adityakadiwal/water-potability' 
+                target='_blank'>Data From Water Quality in Kaggle
+            </a>
+        </p>
+        """
+    )
+
+demo.launch()

model/.gitattributes

@@ -0,0 +1 @@
+*.pkl filter=lfs diff=lfs merge=lfs -text

model/model1.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebd89908c4a005e70df85161aeca87fadf7ef882e5bd317af9f679e713525b4e
+size 39354808

requirements.txt

@@ -0,0 +1,183 @@
+aiofiles==23.2.1
+alembic==1.12.0
+altair==5.1.1
+annotated-types==0.5.0
+anyio==3.7.1
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.2.3
+asttokens==2.4.0
+async-lru==2.0.4
+attrs==23.1.0
+Babel==2.12.1
+backcall==0.2.0
+beautifulsoup4==4.12.2
+bleach==6.0.0
+blinker==1.6.2
+certifi==2023.7.22
+cffi==1.15.1
+cfgv==3.4.0
+charset-normalizer==3.2.0
+click==8.1.7
+cloudpickle==2.2.1
+comm==0.1.4
+contourpy==1.1.1
+cycler==0.11.0
+databricks-cli==0.17.8
+dearpygui==1.10.0
+debugpy==1.8.0
+decorator==5.1.1
+defusedxml==0.7.1
+distlib==0.3.7
+docker==6.1.3
+entrypoints==0.4
+exceptiongroup==1.1.3
+executing==1.2.0
+fastapi==0.103.1
+fastjsonschema==2.18.0
+ffmpy==0.3.1
+filelock==3.12.4
+flake8==6.1.0
+Flask==2.3.3
+fonttools==4.42.1
+fqdn==1.5.1
+fsspec==2023.9.2
+funpymodeling==0.1.8
+gfs==1.0.1
+gitdb==4.0.10
+GitPython==3.1.37
+gradio==3.44.4
+gradio_client==0.5.1
+greenlet==2.0.2
+gunicorn==21.2.0
+h11==0.14.0
+httpcore==0.18.0
+httpx==0.25.0
+huggingface-hub==0.17.2
+identify==2.5.29
+idna==3.4
+importlib-metadata==6.8.0
+importlib-resources==6.1.0
+iniconfig==2.0.0
+ipykernel==6.25.2
+ipython==8.15.0
+ipython-genutils==0.2.0
+ipywidgets==8.1.1
+isoduration==20.11.0
+itsdangerous==2.1.2
+jedi==0.19.0
+Jinja2==3.1.2
+joblib==1.3.2
+json5==0.9.14
+jsonpointer==2.4
+jsonschema==4.19.1
+jsonschema-specifications==2023.7.1
+jupyter==1.0.0
+jupyter-console==6.6.3
+jupyter-events==0.7.0
+jupyter-lsp==2.2.0
+jupyter_client==8.3.1
+jupyter_core==5.3.1
+jupyter_server==2.7.3
+jupyter_server_terminals==0.4.4
+jupyterlab==4.0.6
+jupyterlab-pygments==0.2.2
+jupyterlab-widgets==3.0.9
+jupyterlab_server==2.25.0
+kiwisolver==1.4.5
+Mako==1.2.4
+Markdown==3.4.4
+MarkupSafe==2.1.3
+matplotlib==3.8.0
+matplotlib-inline==0.1.6
+mccabe==0.7.0
+mistune==3.0.1
+mlflow==2.7.1
+nbclient==0.8.0
+nbconvert==7.8.0
+nbformat==5.9.2
+nest-asyncio==1.5.8
+nodeenv==1.8.0
+notebook==7.0.4
+notebook_shim==0.2.3
+numpy==1.26.0
+oauthlib==3.2.2
+orjson==3.9.7
+overrides==7.4.0
+packaging==23.1
+pandas==2.1.1
+pandocfilters==1.5.0
+parso==0.8.3
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==10.0.1
+platformdirs==3.10.0
+pluggy==1.3.0
+pre-commit==3.4.0
+prometheus-client==0.17.1
+prompt-toolkit==3.0.39
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+Pygments==2.16.1
+PyJWT==2.8.0
+pyparsing==3.1.1
+pytest==7.4.2
+python-dateutil==2.8.2
+python-json-logger==2.0.7
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+QtPy==2.4.0
+querystring-parser==1.2.4
+referencing==0.30.2
+requests==2.31.0
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
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+scikit-learn==1.3.1
+scipy==1.11.2
+seaborn==0.12.2
+semantic-version==2.10.0
+Send2Trash==1.8.2
+six==1.16.0
+smmap==5.0.1
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+terminado==0.17.1
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+tinycss2==1.2.1
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+toolz==0.12.0
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+tqdm==4.66.1
+traitlets==5.10.0
+typing_extensions==4.8.0
+tzdata==2023.3
+uri-template==1.3.0
+urllib3==1.26.16
+uvicorn==0.23.2
+virtualenv==20.24.5
+wcwidth==0.2.6
+webcolors==1.13
+webencodings==0.5.1
+websocket-client==1.6.3
+websockets==11.0.3
+Werkzeug==2.3.7
+widgetsnbextension==4.0.9
+zipp==3.17.0