Upload app.py

app.py

@@ -1,28 +1,65 @@
-# Written On: 10 Feb 2025
-# Last Updaate: 10 Feb 2025
+#+--------------------------------------------------------------------------------------------+
+# Breast Cancer Prediction 
+# Using Neural Networks and Tensorflow
+# Prediction using Gradio on Hugging Face
+# Written by: Prakash R. Kota
+# Written on: 12 Feb 2025
+# Last update: 12 Feb 2025
 
-#from transformers.utils import logging
-#logging.set_verbosity_error()
-
-#import warnings
-#warnings.filterwarnings("ignore", 
-#                        message="Using the model-agnostic default `max_length`")
+# Data Set from
+# Original: 
+#     https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic
+# With Header:
+#     https://www.kaggle.com/code/nancyalaswad90/analysis-breast-cancer-prediction-dataset
+#
+# Input Data Format for Gradio must be in the above header format with 30 features
+# The header has 32 features listed, but ignore the first 2 header columns
+#+--------------------------------------------------------------------------------------------+
 
+import tensorflow as tf
+import numpy as np
 import gradio as gr
-from transformers import pipeline
+import joblib
+
+
+# Load the trained model
+model = tf.keras.models.load_model("PRK_BC_NN_Model.keras")
+
+# Load the saved Scaler
+scaler = joblib.load("PRK_BC_NN_Scaler.pkl")
 
-pipe = pipeline("image-to-text",
-                model="Salesforce/blip-image-captioning-base")
+# Function to process input and make predictions
+def predict(input_text):
+    # Convert input string into a NumPy array of shape (1, 30)
+    input_data = np.array([list(map(float, input_text.split(",")))])
 
-def launch(input):
-    out = pipe(input)
-    return out[0]['generated_text']
+    # Ensure the input shape is correct
+    if input_data.shape != (1, 30):
+        return "Error: Please enter exactly 30 numerical values separated by commas."
 
-iface = gr.Interface(launch,
-                     inputs=gr.Image(type='pil'),
-                     outputs="text")
+    # Transform the input data using the loded scaler
+    input_data_scaled = scaler.transform(input_data)
+    
+    # Make a prediction
+    prediction = model.predict(input_data_scaled)
+
+    # Convert prediction to a binary outcome (assuming classification)
+    result = "Malignant" if prediction[0][0] > 0.5 else "Benign"
+
+    return f"Prediction: {result} (Confidence: {prediction[0][0]:.2f})"
+   
+
+import gradio as gr
 
-#iface.launch(share=True, 
-#             server_port=int(os.environ['PORT1']))
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict, 
+    inputs=gr.Textbox(label="Enter 30 feature values, comma-separated"), 
+    outputs="text",
+    title="Breast Cancer Prediction",
+    description="Enter 30 numerical feature values separated by commas to predict whether the biopsy is Malignant or Benign."
+)
 
-iface.launch()
+# Launch the Gradio app
+interface.launch()
+