import streamlit as st
import pandas as pd
import random
import joblib
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import os

# Load cattle data
DATA_FILE = 'cattle_data.csv'

def load_data():
    # Check if the dataset exists
    if os.path.exists(DATA_FILE):
        return pd.read_csv(DATA_FILE)
    else:
        return pd.DataFrame(columns=['TAG ID', 'BREED', 'AGE', 'SURGERY', 'REPRODUCTION CYCLES', 'OTHER'])

# Function to generate random sensor data for a specific cow breed
def generate_sensor_data(cow_breed):
    cow_data = {
        'Holstein Friesian': {'body_temp': (38.0, 39.0), 'rumination': (600, 800), 'steps': (8000, 12000), 'behavior_score': (6, 8)},
        'Girolando': {'body_temp': (38.0, 39.0), 'rumination': (650, 750), 'steps': (7000, 10000), 'behavior_score': (7, 9)},
        'Jersey': {'body_temp': (38.0, 39.0), 'rumination': (650, 750), 'steps': (7500, 10500), 'behavior_score': (6, 8)},
        'Sahiwal': {'body_temp': (38.0, 39.0), 'rumination': (550, 700), 'steps': (6500, 9000), 'behavior_score': (7, 9)},
        'Kankrej': {'body_temp': (38.0, 39.0), 'rumination': (600, 750), 'steps': (7000, 10000), 'behavior_score': (7, 9)}
    }

    data = cow_data.get(cow_breed)
    if not data:
        return None

    sensor_data = {
        'Body Temperature (°C)': round(random.uniform(data['body_temp'][0], data['body_temp'][1]), 2),
        'Rumination (min/day)': random.randint(data['rumination'][0], data['rumination'][1]),
        'Step Count (steps/day)': random.randint(data['steps'][0], data['steps'][1]),
        'Behavior Score (1-10)': random.randint(data['behavior_score'][0], data['behavior_score'][1])
    }
    return sensor_data

# Load the data and preprocess
df = load_data()

# Feature Engineering - Create features like daily trends or differences if needed
df['milk_yield_change'] = df['AGE'].diff().fillna(0)  # Just an example to create a derived feature

# Merge sensor data with cattle profile
cow_breeds = ['Holstein Friesian', 'Girolando', 'Jersey', 'Sahiwal', 'Kankrej']
sensor_data_list = []
for breed in cow_breeds:
    sensor_data = generate_sensor_data(breed)
    if sensor_data:
        sensor_data['TAG ID'] = random.randint(1, 100)  # Random TAG ID for simulation
        sensor_data['BREED'] = breed
        sensor_data_list.append(sensor_data)

sensor_df = pd.DataFrame(sensor_data_list)

# Merge with the original cattle profile dataset
df = pd.merge(df, sensor_df, on='TAG ID', how='left')

# Model Training (Disease Prediction)
X = df[['Body Temperature (°C)', 'Rumination (min/day)', 'Step Count (steps/day)', 'Behavior Score (1-10)', 'AGE']]
y = df['OTHER'].apply(lambda x: 1 if x == 'Healthy' else 0)  # Convert health status to binary (0 = not healthy, 1 = healthy)

# Data Preprocessing
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Train Test Split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)

# Train a Random Forest Classifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Save the trained model
joblib.dump(model, 'disease_prediction_model.pkl')
joblib.dump(scaler, 'scaler.pkl')

# Evaluate the model
y_pred = model.predict(X_test)
st.write("Model Evaluation Results")
st.text(classification_report(y_test, y_pred))

# Streamlit App
st.title('Cattle Profile & Disease Prediction Dashboard')

# Register New Cow form
st.header("➕ Register New Cow")
tag_id = st.number_input("TAG ID", min_value=1, step=1)
breed = st.selectbox("Breed", cow_breeds)
age = st.number_input("Age (in years)", min_value=0, step=1)
surgery = st.text_input("Surgery")
reproduction_cycles = st.number_input("Reproduction Cycles", min_value=0, step=1)
other = st.text_input("Other Health Details")

submit = st.button("Register Cow")
if submit:
    new_cow = pd.DataFrame([{
        'TAG ID': tag_id,
        'BREED': breed,
        'AGE': age,
        'SURGERY': surgery,
        'REPRODUCTION CYCLES': reproduction_cycles,
        'OTHER': other
    }])
    df = pd.concat([df, new_cow], ignore_index=True)
    st.write("Cow registered successfully!")

# Select RFID dropdown to view details
st.header("🔢 Select RFID Tag ID")
selected_rfid = st.selectbox("Select RFID Tag ID", df['TAG ID'].unique())

if selected_rfid:
    cow_info = df[df['TAG ID'] == selected_rfid].iloc[0]
    st.write(f"**TAG ID:** {cow_info['TAG ID']}")
    st.write(f"**BREED:** {cow_info['BREED']}")
    st.write(f"**AGE:** {cow_info['AGE']} years")
    st.write(f"**SURGERY:** {cow_info['SURGERY']}")
    st.write(f"**REPRODUCTION CYCLES:** {cow_info['REPRODUCTION CYCLES']}")
    st.write(f"**OTHER HEALTH DETAILS:** {cow_info['OTHER']}")

    # Predict disease status based on the selected cow's data
    selected_data = scaler.transform([[
        cow_info['Body Temperature (°C)'],
        cow_info['Rumination (min/day)'],
        cow_info['Step Count (steps/day)'],
        cow_info['Behavior Score (1-10)'],
        cow_info['AGE']
    ]])
    disease_prediction = model.predict(selected_data)
    disease_status = "Healthy" if disease_prediction[0] == 1 else "Not Healthy"
    st.write(f"Predicted Health Status: {disease_status}")

# Display cow milk yield trends
st.header("🐄 Cow Milk Yield Trends")
milk_data = {
    'Tag ID': df['TAG ID'],
    'Date': pd.date_range(start='2025-04-01', periods=len(df), freq='D'),
    'Milk Yield (L)': np.random.uniform(10, 22, len(df)),
}
milk_df = pd.DataFrame(milk_data)

# Plot trends
st.subheader('Milk Yield Trend for Each Cow')
plt.figure(figsize=(10, 6))
sns.lineplot(data=milk_df, x='Date', y='Milk Yield (L)', hue='Tag ID', marker='o')
plt.title('Milk Yield Trend (Day-wise for Each Cow)')
plt.xlabel('Date')
plt.ylabel('Milk Yield (L)')
plt.xticks(rotation=45)
st.pyplot()

# Save updated data back to CSV
df.to_csv(DATA_FILE, index=False)