from fastapi import FastAPI
from pydantic import BaseModel
from utils import fetch_news, analyze_sentiment, extract_topics, generate_tts
import random

# Set up the FastAPI server with a name and description
app = FastAPI(title="News Sentiment API", description="Analyze news sentiment for companies")

class CompanyInput(BaseModel):
    """Simple model to validate incoming company name."""
    company: str

@app.post("/analyze")
async def analyze_company(input: CompanyInput):
    """Take a company name and return its news sentiment analysis."""
    company = input.company
    articles_data = fetch_news(company)
    if not articles_data:
        return {"error": f"No articles found for {company}. Check logs for details."}

    articles = []
    sentiments = {"Positive": 0, "Negative": 0, "Neutral": 0}
    positive_articles = []
    negative_articles = []
    neutral_articles = []

    for article in articles_data:
        summary = article["summary"].strip() or article["title"].split(" - ")[0].strip()
        source = article["title"].split(" - ")[-1].strip() if " - " in article["title"] else ""
        if source in summary:
            summary = summary.replace(source, "").strip()
        summary = f"{summary.rstrip(' -')} - {source}"

        sentiment = analyze_sentiment(summary)
        topics = extract_topics(summary)
        sentiments[sentiment] += 1

        title = article["title"].split(" - ")[0].strip()
        if sentiment == "Positive":
            positive_articles.append(title)
        elif sentiment == "Negative":
            negative_articles.append(title)
        else:
            neutral_articles.append(title)

        articles.append({
            "Title": article["title"],
            "Summary": summary,
            "Sentiment": sentiment,
            "Topics": topics,
            "Link": article["link"],
            "PubDate": article["pub_date"]
        })

    detailed_comparisons = [f"- News {i + 1} {article['Sentiment'].lower()}ly discusses {', '.join(article['Topics'])}"
                            for i, article in enumerate(articles)]
    dominant_sentiment = max(sentiments, key=sentiments.get)
    trends = f"{company} News Trends: {dominant_sentiment}"

    total_articles = sum(sentiments.values())
    sentiment_count = f"{sentiments['Positive']} positive, {sentiments['Negative']} negative, {sentiments['Neutral']} neutral"

    intro_phrases = [
        f"Spanning {total_articles} recent reports, the narrative surrounding {company} tilts {dominant_sentiment.lower()}, with {sentiment_count}.",
        f"Across {total_articles} articles in recent coverage, {company}’s story emerges as predominantly {dominant_sentiment.lower()}, reflecting {sentiment_count}.",
        f"Drawing from {total_articles} latest publications, {company}’s news landscape leans {dominant_sentiment.lower()}, underscored by {sentiment_count}."
    ]
    positive_phrases = [
        f"With {len(positive_articles)} favorable accounts, {company} demonstrates notable progress, exemplified by '{random.choice(positive_articles) if positive_articles else 'no specific examples available'}'.",
        f"Boasting {len(positive_articles)} positive developments, {company} showcases strength, as evidenced in '{random.choice(positive_articles) if positive_articles else 'no notable instances'}'.",
        f"Highlighted by {len(positive_articles)} encouraging reports, {company} is forging ahead, with '{random.choice(positive_articles) if positive_articles else 'no standout reports'}' standing out."
    ]
    negative_phrases = [
        f"However, {len(negative_articles)} troubling narratives raise concerns, including '{random.choice(negative_articles) if negative_articles else 'no specific concerns noted'}'.",
        f"Yet, {len(negative_articles)} adverse reports signal challenges, such as '{random.choice(negative_articles) if negative_articles else 'no highlighted issues'}'.",
        f"Nevertheless, {len(negative_articles)} concerning stories cast a shadow, notably '{random.choice(negative_articles) if negative_articles else 'no notable setbacks'}'."
    ]
    neutral_phrases = [
        f"Additionally, {len(neutral_articles)} impartial updates provide context, such as '{random.choice(neutral_articles) if neutral_articles else 'no neutral updates available'}'.",
        f"Meanwhile, {len(neutral_articles)} balanced accounts offer insight, including '{random.choice(neutral_articles) if neutral_articles else 'no balanced reports'}'.",
        f"Furthermore, {len(neutral_articles)} objective pieces contribute details, like '{random.choice(neutral_articles) if neutral_articles else 'no objective details'}'."
    ]
    outlook_phrases_positive = [
        f"In summary, {company} appears poised for a favorable trajectory.",
        f"All told, {company} stands on the cusp of a promising future.",
        f"Ultimately, {company} is positioned for an optimistic course ahead."
    ]
    outlook_phrases_negative = [
        f"In conclusion, {company} confronts a challenging path forward.",
        f"Overall, {company} navigates a formidable road ahead.",
        f"To conclude, {company} faces a demanding horizon."
    ]
    outlook_phrases_mixed = [
        f"In the final analysis, {company} balances opportunity and uncertainty.",
        f"On balance, {company} presents a complex outlook moving forward.",
        f"Ultimately, {company} reflects a blend of prospects and hurdles."
    ]

    final_text = random.choice(intro_phrases) + " "
    if positive_articles:
        final_text += random.choice(positive_phrases) + " "
    if negative_articles:
        final_text += random.choice(negative_phrases) + " "
    if neutral_articles:
        final_text += random.choice(neutral_phrases) + " "
    if sentiments["Positive"] > sentiments["Negative"]:
        final_text += random.choice(outlook_phrases_positive)
    elif sentiments["Negative"] > sentiments["Positive"]:
        final_text += random.choice(outlook_phrases_negative)
    else:
        final_text += random.choice(outlook_phrases_mixed)

    print(f"Generated dynamic final sentiment for {company}: {final_text}")

    return {
        "Company": company,
        "Articles": articles,
        "Comparative Sentiment Score": {
            "Sentiment Distribution": f"Positive: {sentiments['Positive']}, Negative: {sentiments['Negative']}, Neutral: {sentiments['Neutral']}",
            "Trends": trends,
            "Detailed Comparisons": "\n".join(detailed_comparisons)
        },
        "Final Sentiment Analysis": final_text.strip(),
        "Audio": None
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)  # Start the API server on port 8000