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from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy.orm import Session
from database import get_db, save_post
from utils.preprocessing import preprocess_data
from utils.image_processing import extract_text_from_image, analyze_image
import joblib
import requests
from PIL import Image
from io import BytesIO
from textblob import TextBlob
import pandas as pd
# Load models
viral_model = joblib.load("models/viral_potential_model.pkl")
engagement_model = joblib.load("models/engagement_rate_model.pkl")
promotion_model = joblib.load("models/promotion_strategy_model.pkl")
router = APIRouter()
# Endpoint to analyze and save a post
@router.post("/analyze-post")
async def analyze_post(caption: str, hashtags: str, image_url: str, db: Session = Depends(get_db)):
try:
# Download and analyze the image
response = requests.get(image_url)
response.raise_for_status()
image = Image.open(BytesIO(response.content))
extracted_text = extract_text_from_image(image)
image_analysis = analyze_image(image)
# Preprocess input for models
features = {
'caption_length': len(caption),
'hashtag_count': len(hashtags.split(",")),
'sentiment': TextBlob(caption).sentiment.polarity
}
features_df = pd.DataFrame([features])
# Make predictions
viral_score = viral_model.predict_proba(features_df)[0][1]
engagement_rate = engagement_model.predict(features_df)[0]
promote = promotion_model.predict(features_df)[0]
# Save post to database
post_data = {
"caption": caption,
"hashtags": hashtags,
"image_url": image_url,
"engagement_rate": engagement_rate,
"viral_score": viral_score,
"promote": promote
}
save_post(db, post_data)
return {
"extracted_text": extracted_text,
"image_analysis": image_analysis,
"viral_score": viral_score,
"engagement_rate": engagement_rate,
"promote": bool(promote)
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e)) |