Duplicate from jflo/CancerClassification

Co-authored-by: Jesus Flores <jflo@users.noreply.huggingface.co>

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README.md

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+---
+title: CancerClassification
+emoji: 🐠
+colorFrom: yellow
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.27.0
+app_file: app.py
+pinned: false
+duplicated_from: jflo/CancerClassification
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import torch
+import gradio as gr
+import torch.nn.functional as F
+import pandas as pd
+
+df = pd.read_csv("Cancer_Data.csv")
+df = df.dropna(axis='columns')
+df = df.drop(['id'],axis=1)
+
+headers = ["diagnosis","radius_mean","texture_mean","perimeter_mean","area_mean","smoothness_mean","compactness_mean","concavity_mean","concave points_mean","symmetry_mean","fractal_dimension_mean","radius_se","texture_se","perimeter_se","area_se","smoothness_se","compactness_se","concavity_se","concave points_se","symmetry_se","fractal_dimension_se","radius_worst","texture_worst","perimeter_worst","area_worst","smoothness_worst","compactness_worst","concavity_worst","concave points_worst","symmetry_worst","fractal_dimension_worst"]
+inputs = [gr.Dataframe(headers = headers, row_count = (2, "dynamic"), col_count=(31,"dynamic"), label="Input Data", interactive=False)]
+outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(1, "fixed"), label="Predictions", headers=["results"])]
+
+def classify_cell(df_input):
+    # Dropping diagnosis
+    cells = df_input.drop(['diagnosis'],axis=1).values
+    
+    # Classes
+    cancer_classes = ['Benign','Malignant']
+    # Loading model
+    cell_model = torch.jit.load('cancer_classifier.ptl')
+    # List I will pass into a dataframe as the return object
+    cell_results = []
+
+    # Converting to tensor and casting it as float32
+    cell = torch.tensor(cells)
+    cell = cell.to(torch.float)
+
+    # Running through model and applying softmax to output
+    cell_pred = cell_model(cell)
+    cell_pred = F.softmax(cell_pred,dim=1)
+    # Looping through model output to format string for each cell
+    for i in range(len(cell_pred)):
+        cell_prob = round(cell_pred[i][cell_pred[i].argmax()].item()*100,2)
+        output_string = f"{cancer_classes[cell_pred[i].argmax()]} Cancer Cell : {cell_prob}% confident"
+        # Appending formatted string for a cell to cell results list
+        cell_results.append(output_string)
+    return pd.DataFrame(cell_results,columns=["results"])
+
+demo = gr.Interface(classify_cell,
+                        inputs = inputs,
+                        outputs = outputs,
+                        title = "Classify Cancer Cell", 
+                        description="Classifies Cancer Cell into <b>Malignant</b> or <b>Benign</b> based on its features. Click on the example to classify 5 cells! </br> First Column shows what the cell should be classified as",
+                        examples=[df.iloc[17:22]],
+                        cache_examples=False
+                   )
+                    
+demo.launch(inline=False)

requirements.txt

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+torch