Upload ai.py

ai.py

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+# -*- coding: utf-8 -*-
+"""AI.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1nPFOaItiXTX4ZGbA-dJrrXsrvf4yvsAz
+"""
+
+import pandas as pd
+
+dataset = pd.read_csv('cancer.csv')
+
+x = dataset.drop(columns=["diagnosis(1=m, 0=b)"])
+
+y = dataset["diagnosis(1=m, 0=b)"]
+
+from sklearn.model_selection import train_test_split
+
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+
+import tensorflow as tf
+
+model = tf.keras.models.Sequential()
+
+model.add(tf.keras.layers.Dense(256, input_shape=x_train.shape, activation='sigmoid'))
+model.add(tf.keras.layers.Dense(256, activation='sigmoid'))
+model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
+
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+model.fit(x_train, y_train, epochs=1000)
+
+model.evaluate(x_test, y_test)
+
+model.save("percyAI_model.h5")
+
+from google.colab import files
+files.download("percyAI_model.h5")
+
+from tensorflow.keras.models import load_model
+model = load_model("percyAI_model.h5")
+
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+x_values = np.random.randint(0, 100, (10000, 2))
+y_values = np.sum(x_values, axis=1)
+
+x_values = x_values / 100.0
+y_values = y_values / 200.0
+
+model = keras.Sequential([
+    layers.Dense(64, activation='relu', input_shape=(2,)),
+    layers.Dense(64, activation='relu'),
+    layers.Dense(1)
+])
+
+model.compile(optimizer='adam', loss='mse', metrics=['mae'])
+
+model.fit(x_values, y_values, epochs=30, batch_size=32, validation_split=0.2)
+
+test_input = np.array([[25, 30]]) / 100.0
+prediction = model.predict(test_input) * 200.0
+print("Predicted sum:", prediction[0][0])
+
+model.save("percyAI_model.h5")
+
+from google.colab import files
+files.download("percyAI_model.h5")
+
+model.save("percyAI_model.h5")
+
+!pip install gradio
+
+import gradio as gr
+import re
+
+BOT_NAME = "Phu Quy Ho"
+
+# Basic math evaluator (with safety)
+def handle_math(user_input):
+    user_input = user_input.lower().strip()
+
+    # Respond to greetings
+    greetings = ["hi", "hello", "hey", "yo"]
+    if any(greet in user_input for greet in greetings):
+        return f"Hello! I am {BOT_NAME}. I can help you with math. Try something like: (3 + 5) * -2"
+
+    # Remove filler words like "what is"
+    math_expression = re.sub(r"[^\d\.\+\-\*/\(\)\s]", "", user_input)  # Strip out words, keep symbols
+    math_expression = math_expression.replace("what is", "").strip()
+
+    try:
+        result = eval(math_expression)
+        return f"The answer is {result}"
+    except:
+        return "Sorry, I couldn't understand that. Please enter a valid math expression like (2 + 3) * -1"
+
+# Gradio UI
+gr.Interface(
+    fn=handle_math,
+    inputs="text",
+    outputs="text",
+    title="Phu Quy Ho – Smart Math Solver",
+    description="Say hello or ask me a math question! I support +, -, *, /, negative numbers, and parentheses."
+).launch()
+
+model.save("percyAI_model.h5")
+