Labels

155 Change TIpo de Fraude for KInd of Water
156 Change Evalur for Test

app.py

@@ -1,188 +1,188 @@
-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()
+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="Kind of Water")
+            predict_btn = gr.Button(value="Test")
+            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()