import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve, auc

# Sidebar navigation
st.sidebar.title("App Navigation")
page = st.sidebar.radio("Choose a feature", ["Sentiment Analysis", "Model Evaluation"])

# Sentiment Analysis Page
if page == "Sentiment Analysis":
    # Set page title
    st.title("Twitter Sentiment Analysis App")

    # Load sentiment analysis pipeline
    from transformers import pipeline
    sentiment_pipe = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")

    # Input box for user to enter a tweet
    user_input = st.text_input("Enter a tweet to analyze:")

    if user_input:
        # Run sentiment analysis
        result = sentiment_pipe(user_input)
        st.write("Sentiment Analysis Result:", result)

# Model Evaluation Page
elif page == "Model Evaluation":
    st.title("Model Precision-Recall Evaluation")

    # Initialize default values for y_true and y_score
    y_true, y_score = None, None

    # Option to use actual model predictions
    st.write("### Evaluate Using Actual Model")
    model_type = st.selectbox("Choose model type:", ["Scikit-learn", "Transformers"])

    if model_type == "Scikit-learn":
        st.write("### Scikit-learn Model")

        from sklearn.ensemble import RandomForestClassifier
        from sklearn.datasets import make_classification

        # Create and train a Scikit-learn model
        X_train, y_train = make_classification(n_samples=1000, n_features=20, random_state=42)
        X_test, y_test = make_classification(n_samples=200, n_features=20, random_state=42)
        model = RandomForestClassifier()
        model.fit(X_train, y_train)

        # Generate predictions
        y_score = model.predict_proba(X_test)[:, 1]  # Predicted probabilities for the positive class
        y_true = y_test  # True labels

    elif model_type == "Transformers":
        st.write("### Transformers Model")

        from transformers import pipeline

        # Load a Transformers model
        model = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")

        # Simulate a batch of tweets for evaluation
        tweets = ["I love this!", "This is bad...", "I'm not sure about this."]
        results = model(tweets)

        # Simulated true labels (manually defined for simplicity)
        y_true = [1, 0, 1]  # Manually defined true labels
        y_score = [res["score"] for res in results]  # Extract probabilities

    # Validate data and calculate Precision-Recall curve
    if y_true is not None and y_score is not None and len(y_true) > 0 and len(y_score) > 0:
        try:
            # Calculate Precision, Recall, and AUC
            precision, recall, _ = precision_recall_curve(y_true, y_score)
            pr_auc = auc(recall, precision)

            # Plot the PR curve
            fig, ax = plt.subplots()
            ax.plot(recall, precision, label=f"PR Curve (AUC = {pr_auc:.2f})")
            ax.set_xlabel("Recall")
            ax.set_ylabel("Precision")
            ax.set_title("Precision-Recall Curve")
            ax.legend(loc="best")
            ax.grid()

            # Display the plot
            st.pyplot(fig)
        except Exception as e:
            st.error(f"An error occurred while generating the PR curve: {e}")
    else:
        st.info("Please select a model and ensure it generates valid data.")