app.py

from flask import Flask, request, jsonify
import pandas as pd
from transformers import pipeline
import os
import re
import json
import requests
import random
from difflib import get_close_matches
from textblob import TextBlob
from nltk.tokenize import word_tokenize, sent_tokenize
import nltk
import ast  
from urllib.parse import quote

def force_download_nltk():
    nltk_data_dir = os.environ.get("NLTK_DATA", "/app/nltk_data")
    transformers_cache_dir = os.environ.get("TRANSFORMERS_CACHE", "/app/transformers_cache")
    os.makedirs(nltk_data_dir, exist_ok=True)
    os.makedirs(transformers_cache_dir, exist_ok=True)
    os.environ["NLTK_DATA"] = nltk_data_dir
    os.environ["TRANSFORMERS_CACHE"] = transformers_cache_dir
    needed_packages = ["punkt"]
    for package in needed_packages:
        try:
            nltk.data.find(f"tokenizers/{package}")
        except LookupError:
            print(f"Downloading NLTK package: {package} to {nltk_data_dir}")
            nltk.download(package, download_dir=nltk_data_dir)
force_download_nltk()

domain_words = {
    "carb", "carbs", "carbo", "carbohydrate", "carbohydrates",
    "fat", "fats", "protein", "proteins", "fiber", "cholesterol",
    "calcium", "iron", "magnesium", "potassium", "sodium", "vitamin", "vitamin c",
    "calories", "calorie"
}

def smart_correct_spelling(text, domain_set):
    tokens = word_tokenize(text)
    corrected_tokens = []
    for token in tokens:
        if token.isalpha() and token.lower() not in domain_set:
            corrected_word = str(TextBlob(token).correct())
            corrected_tokens.append(corrected_word)
        else:
            corrected_tokens.append(token)
    return " ".join(corrected_tokens)

qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

def summarize_input(text):
    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
    return summary[0]['summary_text']

df = pd.read_csv("Datasets/Final used Datasets/food_dataset_with_nutriition.csv")
print(f"Starting with {len(df)} recipes in dataset")
nutrition_columns = ["calories", "Total fats", "Carbohydrate", "Fiber", "Protein", 
                     "Cholesterol", "Calcium", "Iron", "Magnesium", "Potassium", "Sodium", "Vitamin C"]
for col in nutrition_columns:
    df[col] = pd.to_numeric(df[col], errors='coerce')

disease_df = pd.read_csv("Datasets/Final used Datasets/disease_food_nutrition_mapping.csv")
disease_df["Disease"] = disease_df["Disease"].str.lower()

try:
    with open("docs/common_misspellings.json", "r") as file:
        common_misspellings = json.load(file)
except FileNotFoundError:
    common_misspellings = {"suger": "sugar", "milc": "milk"}
    with open("docs/common_misspellings.json", "w") as file:
        json.dump(common_misspellings, file, indent=2)

try:
    with open("docs/common_ingredients.json", "r") as file:
        common_ingredients = json.load(file)
except FileNotFoundError:
    common_ingredients = ["sugar", "salt", "flour", "milk", "eggs", "butter", "oil", "water"]
    with open("docs/common_ingredients.json", "w") as file:
        json.dump(common_ingredients, file, indent=2)

def create_ingredient_dictionary(dataframe, common_ingredients_list):
    all_ingredients = []
    all_ingredients.extend(common_ingredients_list)
    all_ingredients.extend(set(common_misspellings.values()))
    for ingredients_list in dataframe['ingredients']:
        parts = re.split(r',|\sand\s|\sor\s|;', str(ingredients_list))
        for part in parts:
            clean_part = re.sub(
                r'\d+[\s/]*(oz|ounce|cup|tbsp|tsp|tablespoon|teaspoon|pound|lb|g|ml|l|pinch|dash)\b\.?',
                '', part)
            clean_part = re.sub(
                r'\b(fresh|freshly|chopped|minced|diced|sliced|grated|ground|powdered|crushed|toasted|roasted)\b',
                '', clean_part)
            clean_part = re.sub(r'\(.*?\)', '', clean_part)
            clean_part = clean_part.strip()
            subparts = re.split(r'\sand\s|\sor\s', clean_part)
            for subpart in subparts:
                cleaned_subpart = subpart.strip().lower()
                if cleaned_subpart and len(cleaned_subpart) > 2:
                    all_ingredients.append(cleaned_subpart)
    unique_ingredients = list(set(all_ingredients))
    unique_ingredients.sort(key=len, reverse=True)
    return unique_ingredients
food_dictionary = create_ingredient_dictionary(df, common_ingredients)

def identify_food_ingredient(text, ingredient_dict, misspellings_dict):
    cleaned = re.sub(
        r'\d+[\s/]*(oz|ounce|cup|tbsp|tsp|tablespoon|teaspoon|pound|lb|g|ml|l|pinch|dash)\b\.?',
        '', text)
    cleaned = re.sub(
        r'\b(fresh|freshly|chopped|minced|diced|sliced|grated|ground|powdered|crushed|toasted|roasted)\b',
        '', cleaned)
    cleaned = re.sub(r'\(.*?\)', '', cleaned)
    cleaned = cleaned.strip().lower()
    if cleaned in misspellings_dict:
        return misspellings_dict[cleaned]
    if cleaned in ingredient_dict:
        return cleaned
    words = cleaned.split()
    for word in words:
        if word in ingredient_dict:
            return word
        if word in misspellings_dict:
            return misspellings_dict[word]
    close_matches = get_close_matches(cleaned, ingredient_dict, n=3, cutoff=0.8)
    if close_matches:
        return close_matches[0]
    for dict_ingredient in ingredient_dict:
        if dict_ingredient in cleaned:
            return dict_ingredient
    close_matches = get_close_matches(cleaned, ingredient_dict, n=3, cutoff=0.6)
    if close_matches:
        return close_matches[0]
    return None

def correct_food_ingredient(ingredient, ingredient_dict, misspellings_dict):
    cleaned = re.sub(
        r'\d+[\s/]*(oz|ounce|cup|tbsp|tsp|tablespoon|teaspoon|pound|lb|g|ml|l|pinch|dash)\b\.?',
        '', ingredient)
    cleaned = re.sub(
        r'\b(fresh|freshly|chopped|minced|diced|sliced|grated|ground|powdered|crushed|toasted|roasted)\b',
        '', cleaned)
    cleaned = re.sub(r'\(.*?\)', '', cleaned)
    cleaned = cleaned.strip().lower()
    if cleaned in misspellings_dict:
        return misspellings_dict[cleaned]
    if cleaned in ingredient_dict:
        return cleaned
    close_matches = get_close_matches(cleaned, ingredient_dict, n=3, cutoff=0.8)
    if close_matches:
        return close_matches[0]
    close_matches = get_close_matches(cleaned, ingredient_dict, n=3, cutoff=0.6)
    if close_matches:
        return close_matches[0]
    for dict_ingredient in ingredient_dict:
        if cleaned in dict_ingredient or dict_ingredient in cleaned:
            return dict_ingredient
    return cleaned

def add_misspelling(misspelled, correct):
    try:
        with open("docs/common_misspellings.json", "r") as file:
            misspellings = json.load(file)
        misspellings[misspelled.lower()] = correct.lower()
        with open("docs/common_misspellings.json", "w") as file:
            json.dump(misspellings, file, indent=2, sort_keys=True)
        return True
    except Exception:
        return False

def extract_unwanted_ingredients(input_text):
    question = "What ingredients should be excluded?"
    result = qa_pipeline(question=question, context=input_text)
    raw_answer = result['answer']
    potential_ingredients = []
    for part in raw_answer.split(','):
        for subpart in part.split(' and '):
            for item in subpart.split(' or '):
                clean_item = item.strip()
                if clean_item:
                    potential_ingredients.append(clean_item)
    valid_ingredients = []
    for item in potential_ingredients:
        corrected = identify_food_ingredient(item, food_dictionary, common_misspellings)
        if corrected:
            valid_ingredients.append(corrected)
    return valid_ingredients if valid_ingredients else [raw_answer]

def classify_clause(clause):
    candidate_labels = ["include", "exclude"]
    result = classifier(clause, candidate_labels, hypothesis_template="This clause means the ingredient should be {}.")
    return result["labels"][0].lower()

def extract_ingredients_from_clause(clause, ingredient_dict, misspellings_dict):
    found = []
    for ingredient in ingredient_dict:
        if ingredient.lower() in clause.lower():
            normalized = identify_food_ingredient(ingredient, ingredient_dict, misspellings_dict)
            if normalized:
                found.append(normalized)
    return list(set(found))

def classify_ingredients_in_query(query, ingredient_dict, misspellings_dict):
    include_ingredients = []
    exclude_ingredients = []
    
    nutrition_terms = ['calories', 'calorie', 'fat', 'fats', 'carb', 'carbs', 'protein', 
                       'fiber', 'cholesterol', 'calcium', 'iron', 'magnesium', 
                       'potassium', 'sodium', 'vitamin']
    modified_query = query
    for term in nutrition_terms:
        pattern = re.compile(r'(low|high)\s+' + term, re.IGNORECASE)
        modified_query = pattern.sub('', modified_query)
    clauses = re.split(r'\bbut\b|,', modified_query, flags=re.IGNORECASE)
    for clause in clauses:
        clause = clause.strip()
        if not clause:
            continue
        intent = classify_clause(clause)
        ingredients_found = extract_ingredients_from_clause(clause, ingredient_dict, misspellings_dict)
        if intent == "include":
            include_ingredients.extend(ingredients_found)
        elif intent == "exclude":
            exclude_ingredients.extend(ingredients_found)
    return list(set(include_ingredients)), list(set(exclude_ingredients))

def extract_nutrition_from_clause(clause, nutrition_dict, misspellings_dict):
    found = []
    clause_lower = clause.lower()
    sorted_terms = sorted(nutrition_dict, key=lambda x: -len(x))
    for term in sorted_terms:
        pattern = r'\b' + re.escape(term.lower()) + r'\b'
        if re.search(pattern, clause_lower):
            found.append(term.lower())
    return list(set(found))

def classify_nutrition_in_query(query, nutrition_dict, misspellings_dict):
    include_nutrition = []
    exclude_nutrition = []
    clauses = re.split(r'\band\b|,|but', query, flags=re.IGNORECASE)
    overall_intent = "exclude" if re.search(r'sensitivity|allergy|exclude', query, flags=re.IGNORECASE) else "include"
    for clause in clauses:
        clause = clause.strip()
        if not clause:
            continue
        intent = "include" if "i want" in clause.lower() else overall_intent
        numbers = re.findall(r'\d+(?:\.\d+)?', clause)
        threshold = float(numbers[0]) if numbers else None
        if re.search(r'\b(high|over|above|more than|exceeding)\b', clause, flags=re.IGNORECASE):
            modifier = "high"
        elif re.search(r'\b(low|under|less than|below)\b', clause, flags=re.IGNORECASE):
            modifier = "low"
        else:
            modifier = "high" if intent == "exclude" else "low"
        terms_found = extract_nutrition_from_clause(clause, nutrition_dict, misspellings_dict)
        for term in terms_found:
            norm_term = nutrition_terms_dictionary.get(term, term)
            condition = (modifier, norm_term, threshold) if threshold is not None else (modifier, norm_term)
            if intent == "include":
                include_nutrition.append(condition)
            elif intent == "exclude":
                exclude_nutrition.append(condition)
    return list(set(include_nutrition)), list(set(exclude_nutrition))

nutrition_terms_dictionary = {
    "calorie": "calories",
    "calories": "calories",
    "fat": "Total fats",
    "fats": "Total fats",
    "total fat": "Total fats",
    "total fats": "Total fats",
    "carb": "Carbohydrate",
    "carbs": "Carbohydrate",
    "carbo": "Carbohydrate",
    "carbohydrate": "Carbohydrate",
    "carbohydrates": "Carbohydrate",
    "fiber": "Fiber",
    "protein": "Protein",
    "proteins": "Protein",
    "cholesterol": "Cholesterol",
    "calcium": "Calcium",
    "iron": "Iron",
    "magnesium": "Magnesium",
    "potassium": "Potassium",
    "sodium": "Sodium",
    "vitamin c": "Vitamin C"
}

fixed_thresholds = {
    "calories": 700,
    "Total fats": 60,
    "Carbohydrate": 120,
    "Fiber": 10,
    "Protein": 30,
    "Cholesterol": 100,
    "Calcium": 300,
    "Iron": 5,
    "Magnesium": 100,
    "Potassium": 300,
    "Sodium": 400,
    "Vitamin C": 50
}

def filter_by_nutrition_condition(df, condition):
    if isinstance(condition, tuple):
        if len(condition) == 3:
            direction, nutrition_term, threshold = condition
        elif len(condition) == 2:
            direction, nutrition_term = condition
            threshold = fixed_thresholds.get(nutrition_term)
        else:
            return df
        column = nutrition_term
        if column is None or threshold is None:
            return df
        if direction == "low":
            return df[df[column] < threshold]
        elif direction == "high":
            return df[df[column] >= threshold]
    return df

def score_recipe_ingredients(recipe_ingredients, include_list):
    recipe_lower = recipe_ingredients.lower()
    match_count = sum(
        1 for ingredient in include_list 
        if ingredient.lower() in recipe_lower
    )
    return match_count

def filter_and_rank_recipes(df, include_list, exclude_list, include_nutrition, exclude_nutrition):
    filtered_df = df.copy()
    print(f"Starting with {len(filtered_df)} recipes for filtering")
    if include_list:
        filtered_df['ingredient_match_count'] = filtered_df['ingredients'].apply(
            lambda x: score_recipe_ingredients(str(x), include_list)
        )
        filtered_df = filtered_df[filtered_df['ingredient_match_count'] >= 2]
        print(f"After requiring at least 2 included ingredients: {len(filtered_df)} recipes remain")
    for ingredient in exclude_list:
        before_count = len(filtered_df)
        filtered_df = filtered_df[
            ~filtered_df['ingredients']
            .str.lower()
            .fillna('')
            .str.contains(re.escape(ingredient.lower()))
        ]
        print(f"After excluding '{ingredient}': {len(filtered_df)} recipes remain (removed {before_count - len(filtered_df)})")
    for i, cond in enumerate(include_nutrition):
        before_count = len(filtered_df)
        filtered_df = filter_by_nutrition_condition(filtered_df, cond)
        after_count = len(filtered_df)
        print(f"After applying nutrition condition {i+1} (include) '{cond}': {after_count} recipes remain (removed {before_count - after_count})")
    for i, cond in enumerate(exclude_nutrition):
        before_count = len(filtered_df)
        temp_df = filter_by_nutrition_condition(df.copy(), cond)
        filtered_df = filtered_df[~filtered_df.index.isin(temp_df.index)]
        after_count = len(filtered_df)
        print(f"After applying nutrition condition {i+1} (exclude) '{cond}': {after_count} recipes remain (removed {before_count - after_count})")
    if filtered_df.empty:
        print("\nNo recipes match all criteria. Implementing fallback approach...")
        fallback_df = df.copy()
        if include_list:
            fallback_df['ingredient_match_count'] = fallback_df['ingredients'].apply(
                lambda x: score_recipe_ingredients(str(x), include_list)
            )
            fallback_df = fallback_df[fallback_df['ingredient_match_count'] >= 1]
        else:
            fallback_df['ingredient_match_count'] = 1
        for ingredient in exclude_list:
            fallback_df = fallback_df[
                ~fallback_df['ingredients']
                .str.lower()
                .fillna('')
                .str.contains(re.escape(ingredient.lower()))
            ]
        if fallback_df.empty:
            fallback_df = df.sample(min(5, len(df)))
            fallback_df['ingredient_match_count'] = 0
            print("No matches found. Showing random recipes as a fallback")
        filtered_df = fallback_df
    if 'ingredient_match_count' not in filtered_df.columns:
        filtered_df['ingredient_match_count'] = 0
    filtered_df = filtered_df.sort_values('ingredient_match_count', ascending=False)
    return filtered_df

def get_disease_recommendations(user_text, disease_mapping_df):
    user_text_lower = user_text.lower()
    matches = disease_mapping_df[disease_mapping_df['Disease'].apply(lambda d: d in user_text_lower)]
    if not matches.empty:
        disease_info = matches.iloc[0]
        def safe_parse_list(x):
            if isinstance(x, str):
                try:
                    return ast.literal_eval(x)
                except:
                    return [item.strip() for item in x.split(',') if item.strip()]
            return x
        best_foods = safe_parse_list(disease_info.get("Best_Foods", "[]"))
        worst_foods = safe_parse_list(disease_info.get("Worst_Foods", "[]"))
        best_nutrition = safe_parse_list(disease_info.get("Best_Nutrition", "[]"))
        worst_nutrition = safe_parse_list(disease_info.get("Worst_Nutrition", "[]"))
        recommendations = {
            "Disease": disease_info['Disease'],
            "Best_Foods": best_foods,
            "Worst_Foods": worst_foods,
            "Best_Nutrition": best_nutrition,
            "Worst_Nutrition": worst_nutrition
        }
        return recommendations
    return None

def get_recipe_output(recipe_row):
    recipe_name = recipe_row['title']
    ner_info = recipe_row.get('NER', '')
    try:
        ner_list = json.loads(ner_info)
        ner_str = ", ".join(ner_list)
    except Exception:
        ner_str = ner_info
    nutrition_details = {col: float(recipe_row[col]) for col in nutrition_columns}
    result = {
        "Meal name": recipe_name,
        "NER": ner_str,
        "Nutrition details": nutrition_details
    }
    print(f"Meal name: {recipe_name}")
    print(f"NER: {ner_str}")
    print(f"Nutrition details: {nutrition_details}")
    return result

def process_long_query(query):
    if len(query.split()) > 500:
        print("Long input detected. Summarizing...")
        query = summarize_input(query)
    print(f"Processed Query: \"{query}\"")
    corrected = smart_correct_spelling(query, domain_words)
    sentences = sent_tokenize(corrected)
    aggregated_include = []
    aggregated_exclude = []
    aggregated_include_nutrition = []
    aggregated_exclude_nutrition = []
    for sentence in sentences:
        inc, exc = classify_ingredients_in_query(sentence, food_dictionary, common_misspellings)
        aggregated_include.extend(inc)
        aggregated_exclude.extend(exc)
        inc_nut, exc_nut = classify_nutrition_in_query(sentence, list(nutrition_terms_dictionary.keys()), common_misspellings)
        aggregated_include_nutrition.extend(inc_nut)
        aggregated_exclude_nutrition.extend(exc_nut)
    return corrected, list(set(aggregated_include)), list(set(aggregated_exclude)), \
           list(set(aggregated_include_nutrition)), list(set(aggregated_exclude_nutrition))

def send_to_api(meal_data, parent_id):
    try:
        api_endpoint = "http://54.242.19.19:3000/api/ResturantMenu/add"
        meal_id = random.randint(1000, 9999)
        meal_name = meal_data.get("Meal name", "No meal name available")
        ner_info = meal_data.get("NER", "")
        images_public = "https://kero.beshoy.me/recipe_images/"
        image_path = True
        image_url = ""
        if image_path:
            try:
                image_url = images_public + quote(meal_name, safe="") + ".jpg"
                print(f"Successfully uploaded image to the server for {meal_name}: {image_url}")
            except Exception as cl_err:
                print(f"Error uploading to the server: {cl_err}")
        if not image_url:
            image_url = "https://picsum.photos/200"
        payload = {
            "id": str(meal_id),
            "name": meal_name,
            "description": ner_info,
            "photo": image_url,
            "parentId": parent_id
        }
        print(f"\nSending payload to API: {payload}")
        response = requests.post(api_endpoint, json=payload)
        print(f"API Response for meal {meal_name}: {response.status_code}")

        try:
            return response.json()
        except Exception:
            return {"error": response.text}
    except Exception as e:
        print(f"Error sending meal to API: {e}")
        return {"error": str(e)}

app = Flask(__name__)
@app.route('/process', methods=['POST'])
def process():
    try:

        input_text = ""
        parent_id = ""

        if request.is_json:

            data = request.json
            input_text = data.get("description", "")
            parent_id = data.get("parentId", "")
            
            if not input_text:
                return jsonify({"error": "Missing description in request"}), 400
            if not parent_id:
                return jsonify({"error": "Missing parentId in request"}), 400

        else:

            input_text_json = request.form
            input_text = input_text_json.get("description", "")
            parent_id = input_text_json.get("parentId", "")
            
            if not input_text:
                return jsonify({"error": "Missing description in request"}), 400
            if not parent_id:
                return jsonify({"error": "Missing parentId in request"}), 400

            print("WARNING: Using raw data format. Please consider using JSON format.")

        raw_input_text = input_text
        processed_input, user_include, user_exclude, user_include_nutrition, user_exclude_nutrition = process_long_query(raw_input_text)
        
        include_list, exclude_list = [], []
        include_nutrition, exclude_nutrition = [], []
        
        disease_recs = get_disease_recommendations(processed_input, disease_df)
        
        if disease_recs:
            print("\nDisease-related Recommendations Detected:")
            print(f"Disease: {disease_recs['Disease']}")
            print(f"Best Foods: {disease_recs['Best_Foods']}")
            print(f"Worst Foods: {disease_recs['Worst_Foods']}")
            print(f"Best Nutrition: {disease_recs['Best_Nutrition']}")
            print(f"Worst Nutrition: {disease_recs['Worst_Nutrition']}")
            
            include_list.extend(disease_recs["Best_Foods"])
            exclude_list.extend(disease_recs["Worst_Foods"])

            def parse_nutrition_condition(nutrition_phrase):
                parts = nutrition_phrase.strip().split()
                if len(parts) == 2:
                    direction = parts[0].lower()
                    nutrient = parts[1].lower()
                    mapped_nutrient = nutrition_terms_dictionary.get(nutrient, nutrient)
                    return (direction, mapped_nutrient)
                return None

            for bn in disease_recs["Best_Nutrition"]:
                cond = parse_nutrition_condition(bn)
                if cond:
                    include_nutrition.append(cond)
            for wn in disease_recs["Worst_Nutrition"]:
                cond = parse_nutrition_condition(wn)
                if cond:
                    exclude_nutrition.append(cond)

        include_list.extend(user_include)
        exclude_list.extend(user_exclude)
        include_nutrition.extend(user_include_nutrition)
        exclude_nutrition.extend(user_exclude_nutrition)
        
        include_list = list(set(include_list))
        exclude_list = list(set(exclude_list))
        include_nutrition = list(set(include_nutrition))
        exclude_nutrition = list(set(exclude_nutrition))

        print("\nFinal Lists After Combining Disease + User Query:")
        print(f"Ingredients to include: {include_list}")
        print(f"Ingredients to exclude: {exclude_list}")
        print(f"Nutrition conditions to include: {include_nutrition}")
        print(f"Nutrition conditions to exclude: {exclude_nutrition}")

        corrected_include = [correct_food_ingredient(ingredient, food_dictionary, common_misspellings) for ingredient in include_list]
        corrected_exclude = [correct_food_ingredient(ingredient, food_dictionary, common_misspellings) for ingredient in exclude_list]

        include_list = list(set(corrected_include))
        exclude_list = list(set(corrected_exclude))
        filtered_df = filter_and_rank_recipes(
            df, 
            include_list, 
            exclude_list, 
            include_nutrition, 
            exclude_nutrition
        )
        
        final_output = {}
        api_responses = []
        
        if not filtered_df.empty:
            filtered_df = filtered_df.sample(frac=1)
            meal_count = min(6, len(filtered_df))
            
            for i in range(meal_count):
                if i == 0:
                    print("\nRecommended Meal:")
                    meal_data = get_recipe_output(filtered_df.iloc[i])
                    final_output["Recommended Meal"] = meal_data
                else:
                    print(f"\nOption {i}:")
                    meal_data = get_recipe_output(filtered_df.iloc[i])
                    final_output[f"Option {i}"] = meal_data
                
                api_response = send_to_api(meal_data, parent_id)
                api_responses.append(api_response)
        else:
            error_message = f"No recipes found that match your criteria.\nIngredients to include: {', '.join(include_list)}\nIngredients to exclude: {', '.join(exclude_list)}\nNutrition Include: {', '.join(str(cond) for cond in include_nutrition)}\nNutrition Exclude: {', '.join(str(cond) for cond in exclude_nutrition)}."
            print(error_message)
            final_output["Message"] = error_message
            return jsonify({"error": error_message}), 404
        
        return jsonify({
            "original_response": final_output,
            "api_responses": api_responses,
            "message": f"Successfully processed {len(api_responses)} meals"
        })
    
    except Exception as e:
        print(f"Error processing request: {str(e)}")
        return jsonify({"error": f"Internal server error: {str(e)}"}), 500


if __name__ == '__main__':
    port = int(os.environ.get("PORT", 7860))
    app.run(host="0.0.0.0", port=port, debug=False)