Update app.py

app.py

@@ -1,65 +1,26 @@
-#+--------------------------------------------------------------------------------------------+
-# 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
+# Written On: 10 Feb 2025
+# Last Updaate: 10 Feb 2025
 
-# 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
-#+--------------------------------------------------------------------------------------------+
+#from transformers.utils import logging
+#logging.set_verbosity_error()
 
-import tensorflow as tf
-import numpy as np
-import gradio as gr
-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")
-
-# 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(",")))])
+#import warnings
+#warnings.filterwarnings("ignore", 
+#                        message="Using the model-agnostic default `max_length`")
 
-    # Ensure the input shape is correct
-    if input_data.shape != (1, 30):
-        return "Error: Please enter exactly 30 numerical values separated by commas."
-
-    # 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"
+import gradio as gr
+from transformers import pipeline
 
-    return f"Prediction: {result} (Confidence: {prediction[0][0]:.2f})"
-   
+ pipe = pipeline("image-to-text",
+                model="./models/Salesforce/blip-image-captioning-base")
 
-import gradio as gr
+ def launch(input):
+    out = pipe(input)
+    return out[0]['generated_text']
 
-# 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 = gr.Interface(launch,
+                     inputs=gr.Image(type='pil'),
+                     outputs="text")
 
-# Launch the Gradio app
-interface.launch()
+ iface.launch()