main.py

from fastapi import FastAPI, HTTPException
import numpy as np
import matplotlib.pyplot as plt
from io import BytesIO
from google.cloud import storage
import os

app = FastAPI()

# Google Cloud Storage settings
GCS_BUCKET_NAME = "your-bucket-name"  # Replace with your GCS bucket name
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/your/service-account-key.json"  # Replace with your service account key path

class FinancialAnalyzer:
    def __init__(self, categories, hardcoded_limits):
        self.categories = categories
        self.hardcoded_limits = hardcoded_limits

    def generate_spending_data(self):
        spending_data = {}
        for category in self.categories:
            avg_daily_limit = self.hardcoded_limits[category] / 100
            spending_data[category] = np.random.uniform(0.8 * avg_daily_limit, 1.2 * avg_daily_limit, 100)
        return spending_data

    def analyze_spending(self, spending_data):
        overspent_categories = []
        category_to_avoid = None
        largest_excess = 0

        analysis_result = {"overspent_categories": [], "category_to_avoid": None, "details": {}}

        for category, daily_spending in spending_data.items():
            avg_daily_spending = np.mean(daily_spending)
            hardcoded_limit = self.hardcoded_limits[category] / 100

            analysis_result["details"][category] = {
                "average_daily_spending": avg_daily_spending,
                "daily_limit": hardcoded_limit,
                "status": "Within limit"
            }

            if avg_daily_spending > 1.10 * hardcoded_limit:
                overspent_categories.append(category)
                excess = avg_daily_spending - hardcoded_limit
                if excess > largest_excess:
                    largest_excess = excess
                    category_to_avoid = category
                analysis_result["details"][category]["status"] = "Overspending"

        analysis_result["overspent_categories"] = overspent_categories
        analysis_result["category_to_avoid"] = category_to_avoid

        return analysis_result

    def estimate_days_left(self, spending_data, current_balance):
        avg_daily_spending_all = np.mean([np.mean(spending) for spending in spending_data.values()])
        days_left = current_balance / avg_daily_spending_all if avg_daily_spending_all != 0 else float('inf')
        return days_left

    def plot_spending_trends(self, spending_data, days_left):
        plt.figure(figsize=(12, 8))
        for category, daily_spending in spending_data.items():
            plt.plot(range(1, 101), daily_spending, label=f"{category} (Actual)")
            extended_days = int(np.ceil(days_left))
            extended_spending = daily_spending[:extended_days]
            if len(extended_spending) < extended_days:
                extended_spending = np.concatenate([extended_spending, np.full(extended_days - len(extended_spending), daily_spending[-1])])
            plt.plot(range(101, 101 + extended_days), extended_spending, '--', label=f"{category} (Continued)")

        plt.axvline(x=100, color='red', linestyle='--', label="End of 100 Days")
        plt.xlabel("Days")
        plt.ylabel("Daily Spending ($)")
        plt.title("Spending Trends with Continued Spending until Balance Exhausts")
        plt.legend()
        plt.grid(True)

        # Save the plot to a BytesIO object
        buf = BytesIO()
        plt.savefig(buf, format="png")
        plt.close()
        buf.seek(0)
        return buf

def upload_to_gcs(bucket_name, file_name, file_data):
    """Uploads a file to Google Cloud Storage and makes it publicly accessible."""
    client = storage.Client()
    bucket = client.bucket(bucket_name)
    blob = bucket.blob(file_name)
    blob.upload_from_file(file_data, rewind=True)

    # Make the file publicly accessible
    blob.make_public()
    return blob.public_url

# Initialize the financial analyzer
categories = ['Food', 'Entertainment', 'Shopping', 'Rent', 'Travel']
hardcoded_limits = {
    'Food': 10000,
    'Entertainment': 1000,
    'Shopping': 4000,
    'Rent': 2600,
    'Travel': 3000
}
analyzer = FinancialAnalyzer(categories, hardcoded_limits)

@app.get("/analyze")
async def analyze(current_balance: float):
    # Generate spending data
    spending_data = analyzer.generate_spending_data()

    # Analyze spending
    analysis_result = analyzer.analyze_spending(spending_data)

    # Estimate days left
    days_left = analyzer.estimate_days_left(spending_data, current_balance)
    analysis_result["days_left"] = days_left

    # Generate the plot
    plot_buffer = analyzer.plot_spending_trends(spending_data, days_left)

    # Upload the plot to Google Cloud Storage
    file_name = f"spending_trends_{np.random.randint(1000, 9999)}.png"
    try:
        image_url = upload_to_gcs(GCS_BUCKET_NAME, file_name, plot_buffer)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to upload image to GCS: {str(e)}")

    # Return the GCS URL
    return {"analysis_result": analysis_result, "image_url": image_url}