app.py

from flask import Flask, jsonify, request
import requests
import redis
import json
from flask_cors import CORS
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from history import load_dataset, get_unique_next_words_from_dataset
from dotenv import load_dotenv
import os

os.environ["TRANSFORMERS_CACHE"] = "/code/.cache"


from typing import List, Dict, Optional, Union
import logging
from most_repeted_sentences import sentences_name, get_most_repeated_sentences, save_most_repeated_sentences

load_dotenv()

app = Flask(__name__)
CORS(app)

# Setup logging
logging.basicConfig(level=logging.ERROR)
logger = logging.getLogger(__name__)

# Pixabay API setup
PIXABAY_URL = "https://pixabay.com/api/?key=${pixabayApiKey}&q=${word}&image_type=all&per_page=3"
PIXABAY_API_KEY =os.getenv("API_kEY")


# setup redis
redis_client = redis.Redis(
    host='redis-18594.c301.ap-south-1-1.ec2.redns.redis-cloud.com',
    port=18594,
    decode_responses=True,
    username="default",
    password=os.getenv("REDIS_PASSWORD")
)
print(redis_client)

# Load the model and tokenizer once when the app starts
model = GPT2LMHeadModel.from_pretrained("gpt2").to("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Global variables
predicted_words = []
append_list = []
global_count=0

default_predicted_words = ['i', 'what', 'hello', 'where', 'who', 'how', 'can', 'is', 'are', 'could', 
 'would', 'may', 'can', 'please', 'will', 'shall', 'did', 'have', 'has', 
 'had', 'am', 'were', 'was', 'should', 'might', 'must', 'please', 'you', 
 'he', 'she', 'they', 'it', 'this', 'that', 'these', 'those', 'let', 
 'we', 'my', 'your', 'his', 'her', 'their', 'our', 'the', 
 'there', 'come', 'go', 'bring', 'take', 'give', 'help', 'want', 
 'need', 'eat', 'drink', 'sleep', 'play', 'run', 'walk', 'talk', 'call', 
 'find', 'make', 'see', 'get', 'know']

def generate_predicted_words(input_text,index =0):
    # Load the dataset
    dataset_name = "dataset.txt"
    dataset = load_dataset(dataset_name)

    history_next_text = get_unique_next_words_from_dataset(input_text, dataset)

    # Tokenize input
    inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

    # Forward pass through the model
    with torch.no_grad():
        outputs = model(**inputs, return_dict=True)
        logits = outputs.logits

    # Get the logits for the last token
    last_token_logits = logits[:, -1, :]
    probabilities = torch.softmax(last_token_logits, dim=-1)

    # Get the top 50 most probable next tokens
    top_50_probs, top_50_indices = torch.topk(probabilities, 50)
    top_50_tokens = [tokenizer.decode([idx], clean_up_tokenization_spaces=False) for idx in top_50_indices[0]]

    words = []
    removable_words = [' (', ' a', "'s", ' "', ' -', ' as', " '", "the", " the", "an", " an", "<|endoftext|>", '’d','’m', '’ll','t’s' ,]

    for token in top_50_tokens:
        if len(token) != 1 and token not in removable_words:
            words.append(token.strip().lower())

    return history_next_text + words


# fetch from pixabay
def fetch_images_from_pixabay(query: str) -> dict:
    # print("yo query ko lagi fetch hudai xa..." , query)
    response = requests.get(PIXABAY_URL, params={
        "key": PIXABAY_API_KEY,
        "q": query,
        "image_type": "all",
        "per_page": "3"
    })
    # print("this is from pixabay haita====>" , response.json())
    if response.status_code != 200:
        return {"error": "Failed to fetch data from Pixabay"}
    return response.json()


# fetch images api
@app.route('/api/images', methods=['GET'])
def get_images():
    query = request.args.get('query')
    correspond_id=request.args.get('id')
    print("yo chai id hai" , correspond_id)
    print("yo chai query ho hai" , query)
    if not query:
        return jsonify({"error": "Query parameter is required"}), 400 
    
    # Check Redis cache for images
    cached_images = redis_client.get('image_cache')
    # print("yo ho chaiyeko cached heloooooooooooooooooooooo", cached_images)
    if cached_images:
        cached_images = json.loads(cached_images)  # Convert JSON string to dictionary
        # print("cached_img" , cached_images)
        for i in cached_images['hits']:
            # print("lagalagalag------------>",i.get('query_id'))

            # compare the id of the already queried id and id of the query currently
            if(i.get('query_id')==correspond_id):
                print("Fetching from cache-------------->" , i['previewURL'])
                return jsonify(i['previewURL'])
    
    # print("Fetching from Pixabay")
    # Fetch from Pixabay if not in cache
    # Fetch from Pixabay if not in cache
    data = fetch_images_from_pixabay(query)
    if "error" in data:
        return jsonify(data), 500
    
    for i in data['hits']:
        i['query_id']=correspond_id
        # print("i bhitra haita",i['query_id'])

    # get the total images i.e previously cached images and current images.
    if cached_images:
       data['hits'] = cached_images['hits'] + data['hits']
       data['total'] = cached_images['total'] + data['total']

    # Cache the result in Redis for 1hrs
    redis_client.setex('image_cache', 86400, json.dumps(data))
    
    print("image from Pixabay-------------->" , data['hits'].pop()['previewURL'])
    return jsonify(data['hits'].pop()['previewURL'])

@app.route('/api/display_words', methods=['GET'])
def get_display_words():
    try:
        count = int(request.args.get('count', global_count)) # Default to 0 if 'count' is not provided
        print(type(count))
    except ValueError:
        return jsonify({"error": "Invalid count value"}), 400

    print("Count:", count)
    start_index = 9 * count
    end_index = start_index + 9
    print("Start index:", start_index)
    print("End index:", end_index)
    if start_index >= len(default_predicted_words):  # Reset if out of bounds
        count = 0
        start_index = 0
        end_index = 9

    display_words = default_predicted_words[start_index:end_index]
    print("Display words:", display_words)
    return display_words



# @app.route('/api/scenerio', methods=['POST'])
# # @app.route('/api/select_location', methods=['GET'])
# def scenerio():
#     # Get the query parameter from the URL, e.g., /api/select_location?place=home
#     place = request.args.get('place')
#     if place == "home":
    


    # display_words = default_predicted_words[start_index:end_index]
    # return jsonify(display_words)


# @app.route('/api/huu', methods=['GET'])
# def fetch_most_repeated_sentences():  # Ensure the function name is unique
#     try:
#         with open('most_repeated_sentences.txt', 'r') as file:
#             # Read the first 5 lines
#             lines = []
#             for _ in range(5):
#                 text = file.readline().strip().split(":")[0]
#                 print(text)
#                 lines.append(text)
                
#             # lines = [file.readline().strip().split(':')[0] for _ in range(5)]
        
#         return jsonify(lines), 200  # Return the lines as JSON with a 200 OK status
#     except FileNotFoundError:
#         return jsonify({"error": "File not found."}), 404  # Handle file not found error
#     except Exception as e:
#         return jsonify({"error": str(e)}), 500  # Handle other potential errors

@app.route('/api/most_repeated_sentence', methods=['GET'])
def fetch_most_repeated_sentences():
    try:
        sentences = []
        with open('most_repeated_sentences.txt', 'r') as file:
            for line in file:
                line = line.strip()
                if ':' in line:  # Check if line contains the separator
                    try:
                        sentence, count = line.rsplit(':', 1)  # Split from right side
                        count = int(count.strip())
                        sentences.append((sentence.strip(), count))
                    except (ValueError, IndexError):
                        continue  # Skip invalid lines
        
        # Sort sentences by count in descending order
        sorted_sentences = sorted(sentences, key=lambda x: x[1], reverse=True)
        
        # Get top 5 sentences only
        top_5_sentences = [sentence[0] for sentence in sorted_sentences[:5]]
        
        return jsonify(top_5_sentences)
    
    except FileNotFoundError:   
        return jsonify({"error": "File not found"}), 404
    except Exception as e:
        logger.error(f"Error in fetch_most_repeated_sentences: {e}")
        return jsonify({"error": "Internal server error"}), 500

@app.route('/api/guu', methods=['POST'])
def predict_words():
    global predicted_words, append_list , global_count

    try:
        data = request.get_json()
        print("Received data:", data)

        if not isinstance(data, dict):
            return jsonify({'error': 'Invalid JSON format'}), 400
        
        input_text = data.get('item', '').strip()  # Ensure we are checking the stripped input

        # # Handle case when input_text is "1"
        # if input_text == "1":
        #     print("Resetting append_list")
        #     append_list = []  # Reset the append list
        #     return jsonify(default_predicted_words[:9])  # Return the default words


        # Handle reset request
        if input_text == "1":
        
            with open('dataset.txt', 'a') as file:
                file.write(' '.join(append_list) + '\n')
            
            append_list = []
            global_count = 0
            sentence= sentences_name('dataset.txt')
            repeated_sentences = get_most_repeated_sentences(sentence)
            print("Most repeated sentences:", repeated_sentences)
            save_most_repeated_sentences(repeated_sentences, 'most_repeated_sentences.txt')
            
            return jsonify(default_predicted_words[:9])
        
        if not input_text:
            return jsonify({'error': 'No input text provided'}), 400
        

        append_list.append(input_text)
        print("Current append list:", append_list)
        combined_input = ' '.join(append_list)
        print("Combined input for prediction:", combined_input)
        predicted_words = generate_predicted_words(combined_input)
        print("Predicted words:", predicted_words)

        return jsonify(predicted_words[:9])

    except Exception as e:
        print(f"An error occurred: {str(e)}")  # Log the error message
        return jsonify({'error': str(e)}), 500

application = app
if __name__ == '__main__':
    application.run()