app.py

import os
from transformers import pipeline
from flask_cors import CORS
from flask import Flask, request, json
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin
import google.generativeai as genai
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_google_genai import ChatGoogleGenerativeAI
from dotenv import load_dotenv

os.environ["CUDA_VISIBLE_DEVICES"] = ""

app = Flask(__name__)
cors = CORS(app)
load_dotenv()

# # Define the model and feature extractor globally
# model = AutoModelForImageClassification.from_pretrained('carbon225/vit-base-patch16-224-hentai')
# feature_extractor = AutoFeatureExtractor.from_pretrained('carbon225/vit-base-patch16-224-hentai')

def load_model():  
  api_key=os.getenv("GOOGLE_API_KEY")
  genai.configure(api_key=api_key)
  model = ChatGoogleGenerativeAI(model="gemini-pro",
                             temperature=0.3)
  
  return model

def load_embeddings():
  embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")

  return embeddings

@app.route("/", methods=["GET"])
def default():
    return json.dumps({"Server": "Working"})

@app.route("/extractimages",methods=["GET"])
def extract_images():
    try:
        src=request.args.get("src")
        response = requests.get(src)
        soup = BeautifulSoup(response.content,'html.parser')
        img_urls=[]

        img_tags = soup.select('div img')
        for img_tag in img_tags:
            img_url = urljoin(src, img_tag['src'])
            img_urls.append(img_url)
        return json.dumps({"images":img_urls})
    except Exception as e:
        return e
      
api_key=os.getenv("GOOGLE_API_KEY")
genai.configure(api_key=api_key)
model=genai.GenerativeModel('gemini-pro')
sentiment_analysis = pipeline("sentiment-analysis",model="siebert/sentiment-roberta-large-english")

# @app.route('/sentiment',methods=['POST'])
# def sentiment():
  

# @app.route("/predict", methods=["GET"])
# def predict():
#     try:
#         src = request.args.get("src")

#         # Download image from the provided URL
#         response = requests.get(src)
#         response.raise_for_status()  

#         # Open and preprocess the image
#         image = Image.open(BytesIO(response.content))
#         image = image.resize((128, 128))

#         # Extract features using the pre-trained feature extractor
#         encoding = feature_extractor(images=image.convert("RGB"), return_tensors="pt")

#         # Make a prediction using the pre-trained model
#         with torch.no_grad():
#             outputs = model(**encoding)
#             logits = outputs.logits

#         # Get the predicted class index and label
#         predicted_class_idx = logits.argmax(-1).item()
#         predicted_class_label = model.config.id2label[predicted_class_idx]

#         # Return the predictions
#         return json.dumps({"class": predicted_class_label})

#     except requests.exceptions.RequestException as e:
#         return json.dumps({"error": f"Request error: {str(e)}"})
#     except Exception as e:
#         return json.dumps({"error": f"An unexpected error occurred: {str(e)}"})
    
@app.route('/answer',methods=['POST'])
def answer():
  query=request.get_json()['query']
  final_query=f"""
  Following are negative reviews about my products, suggest what are the key issues from the customer feedback:{query}
  """
  response = model.generate_content(final_query)
  return json.dumps({"message":response.text})

if __name__ == "__main__":
    app.run(debug=True)