import numpy as np
from sklearn.linear_model import LinearRegression

# Let's predict exam scores based on hours studied!
# X = Hours Studied (Our feature)
# y = Exam Score (What we want to predict)
X = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]).reshape(-1, 1)
y = np.array([55, 60, 65, 70, 75, 80, 85, 90, 95])

# 1. Initialize the Linear Regression model
model = LinearRegression()

# 2. Train the model (it learns the relationship between X and y)
model.fit(X, y)

# 3. Make a prediction!
# What score would someone get if they studied 7.5 hours?
predicted_score = model.predict(np.array([[7.5]]))

print(f"If you study 7.5 hours, your predicted score is: {predicted_score[0]:.2f}")
# Output: If you study 7.5 hours, your predicted score is: 82.50

# ✨ Your AI Project Power-Up! ✨
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline

# Sample data (your project's text and categories)
texts = [
    "This movie was fantastic, highly recommend!",
    "Terrible service, wasted my money.",
    "The product works perfectly.",
    "Customer support was unhelpful and rude.",
    "Absolutely loved the experience!"
]
labels = ["positive", "negative", "positive", "negative", "positive"]

# Create a simple text classification pipeline
# TfidfVectorizer converts text to numbers
# LogisticRegression is our classification model
model = make_pipeline(TfidfVectorizer(), LogisticRegression())

# Train your model with your data
model.fit(texts, labels)

# Make a prediction on new text!
new_review = ["This is the worst thing I've ever seen."]
prediction = model.predict(new_review)

print(f"The predicted sentiment is: {prediction[0]}")
# Output for new_review: The predicted sentiment is: negative

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from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 1. Load the dataset
data = load_iris()
X, y = data.data, data.target

# 2. Split into Training data (80%) and Test data (20%)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 3. Initialize the ML model (Random Forest)
model = RandomForestClassifier()

# 4. Train the model
model.fit(X_train, y_train)

# 5. Predict and check accuracy
predictions = model.predict(X_test)
print(f"Model Accuracy: {accuracy_score(y_test, predictions) * 100:.2f}%")

import ollama

# 1. Define your prompt
user_prompt = "Explain the difference between a list and a tuple in Python."

print("🤖 Local AI is thinking...\n")

# 2. Call the locally hosted model
response = ollama.chat(
    model='llama3', 
    messages=[{'role': 'user', 'content': user_prompt}]
)

# 3. Print the result
print("💡 AI Response:")
print(response['message']['content'])