import gradio as gr
from transformers import pipeline
import openai  # Import if you are using OpenAI's API
import random
from datetime import datetime

# Initialize sentiment analysis pipeline
sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")

class JournalCompanion:
    def __init__(self):
        self.entries = []
    
    def analyze_entry(self, entry_text):
        if not entry_text.strip():
            return ("Please write something in your journal entry.", "", "", "")

        try:
            # Perform sentiment analysis
            sentiment_result = sentiment_analyzer(entry_text)[0]
            sentiment = sentiment_result["label"].upper()
            sentiment_score = sentiment_result["score"]
        except Exception as e:
            print("Error during sentiment analysis:", e)
            return (
                "An error occurred during analysis. Please try again.",
                "Error",
                "Could not generate prompts due to an error.",
                "Could not generate affirmation due to an error."
            )

        entry_data = {
            "text": entry_text,
            "timestamp": datetime.now().isoformat(),
            "sentiment": sentiment,
            "sentiment_score": sentiment_score
        }
        self.entries.append(entry_data)

        # Generate dynamic responses using a language model
        prompts = self.generate_dynamic_prompts(sentiment)
        affirmation = self.generate_dynamic_affirmation(sentiment)
        sentiment_percentage = f"{sentiment_score * 100:.1f}%"
        message = f"Entry analyzed! Sentiment: {sentiment} ({sentiment_percentage} confidence)"
        
        return message, sentiment, prompts, affirmation

    def generate_dynamic_prompts(self, sentiment):
        prompt_request = f"Generate three reflective journal prompts for a person feeling {sentiment.lower()}."
        try:
            response = openai.Completion.create(
                engine="gpt-3.5-turbo",
                prompt=prompt_request,
                max_tokens=60,
                n=1
            )
            prompts = response.choices[0].text.strip()
        except Exception as e:
            print("Error generating prompts:", e)
            prompts = "Could not generate prompts at this time."
        return prompts

    def generate_dynamic_affirmation(self, sentiment):
        affirmation_request = f"Generate an affirmation for someone who is feeling {sentiment.lower()}."
        try:
            response = openai.Completion.create(
                engine="gpt-3.5-turbo",
                prompt=affirmation_request,
                max_tokens=20,
                n=1
            )
            affirmation = response.choices[0].text.strip()
        except Exception as e:
            print("Error generating affirmation:", e)
            affirmation = "Could not generate an affirmation at this time."
        return affirmation

    def get_monthly_insights(self):
        if not self.entries:
            return "No entries yet to analyze."
            
        total_entries = len(self.entries)
        positive_entries = sum(1 for entry in self.entries if entry["sentiment"] == "POSITIVE")
        
        insights = f"""Monthly Insights:
        Total Entries: {total_entries}
        Positive Entries: {positive_entries} ({(positive_entries / total_entries * 100):.1f}%)
        Negative Entries: {total_entries - positive_entries} ({((total_entries - positive_entries) / total_entries * 100):.1f}%)
        """
        return insights

def create_journal_interface():
    journal = JournalCompanion()
    
    with gr.Blocks(title="AI Journal Companion") as interface:
        gr.Markdown("# 📔 AI Journal Companion")
        gr.Markdown("Write your thoughts and receive AI-powered insights, prompts, and affirmations.")
        
        with gr.Row():
            with gr.Column():
                entry_input = gr.Textbox(
                    label="Journal Entry",
                    placeholder="Write your journal entry here...",
                    lines=5
                )
                submit_btn = gr.Button("Submit Entry", variant="primary")

            with gr.Column():
                result_message = gr.Markdown(label="Analysis Result")
                sentiment_output = gr.Textbox(label="Detected Sentiment")
                prompt_output = gr.Markdown(label="Reflective Prompts")
                affirmation_output = gr.Textbox(label="Daily Affirmation")
                
        with gr.Row():
            insights_btn = gr.Button("Show Monthly Insights")
            insights_output = gr.Markdown(label="Monthly Insights")

        submit_btn.click(
            fn=journal.analyze_entry,
            inputs=[entry_input],
            outputs=[
                result_message,
                sentiment_output,
                prompt_output,
                affirmation_output
            ]
        )
        
        insights_btn.click(
            fn=journal.get_monthly_insights,
            inputs=[],
            outputs=[insights_output]
        )

    return interface

if __name__ == "__main__":
    interface = create_journal_interface()
    interface.launch()