this is my qrit api , for personal use

Dockerfile

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+# Use an official Python runtime
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy app code
+COPY app.py /app/app.py
+COPY requirements.txt /app/requirements.txt
+
+# Install dependencies
+RUN pip install --upgrade pip && pip install -r requirements.txt
+
+# Expose the port
+EXPOSE 7860
+
+# Start the Flask app
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import os
+import re
+import logging
+from datetime import datetime
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = Flask(__name__)
+CORS(app)
+
+# Model loading
+MODEL_PATH = os.environ.get('MODEL_PATH', 'samdak93/qrit-2')
+tokenizer = None
+model = None
+device = None
+
+def load_model():
+    global tokenizer, model, device
+    logger.info(f"Loading model from {MODEL_PATH}")
+    try:
+        tokenizer = GPT2Tokenizer.from_pretrained(MODEL_PATH)
+        model = GPT2LMHeadModel.from_pretrained(MODEL_PATH)
+    except Exception as e:
+        logger.error(f"Error loading custom model: {e}")
+        logger.info("Falling back to default GPT-2")
+        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+        model = GPT2LMHeadModel.from_pretrained("gpt2")
+
+    tokenizer.pad_token = tokenizer.eos_token
+    model.config.pad_token_id = model.config.eos_token_id
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    logger.info(f"Model loaded on device: {device}")
+
+def generate_recipe(goal_prompt, max_length=300, temperature=0.7):
+    global tokenizer, model, device
+    if tokenizer is None or model is None:
+        load_model()
+
+    full_prompt = f"### Goal: {goal_prompt}\n### Recipe: "
+    input_ids = tokenizer.encode(full_prompt, return_tensors="pt").to(device)
+    attention_mask = torch.ones_like(input_ids).to(device)
+
+    with torch.no_grad():
+        output = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            max_length=max_length,
+            temperature=temperature,
+            do_sample=True,
+            top_k=50,
+            top_p=0.95,
+            no_repeat_ngram_size=2,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    recipe_part = generated_text[len(full_prompt):]
+    if "### Goal:" in recipe_part:
+        recipe_part = recipe_part.split("### Goal:")[0].strip()
+
+    return full_prompt + recipe_part
+
+def parse_recipe(recipe_text):
+    recipe_data = {
+        "title": "",
+        "ingredients": [],
+        "instructions": [],
+        "nutrition": {}
+    }
+
+    title_match = re.search(r"Title: (.*?)(?:\n|$)", recipe_text)
+    if title_match:
+        recipe_data["title"] = title_match.group(1).strip()
+
+    ingredients_section = re.search(r"Ingredients:(.*?)Instructions:", recipe_text, re.DOTALL)
+    if ingredients_section:
+        ingredients_text = ingredients_section.group(1).strip()
+        ingredients_list = [item.strip().lstrip('- ') for item in ingredients_text.split('\n') if item.strip()]
+        recipe_data["ingredients"] = ingredients_list
+
+    instructions_section = re.search(r"Instructions:(.*?)(?:Nutrition:|$)", recipe_text, re.DOTALL)
+    if instructions_section:
+        instructions_text = instructions_section.group(1).strip()
+        instructions_list = []
+        for line in instructions_text.split('\n'):
+            line = line.strip()
+            if line:
+                line = re.sub(r"^\d+\.\s", "", line)
+                instructions_list.append(line.strip())
+        recipe_data["instructions"] = instructions_list
+
+    nutrition_section = re.search(r"Nutrition: (.*?)(?:\n|$)", recipe_text)
+    if nutrition_section:
+        nutrition_text = nutrition_section.group(1).strip()
+        for label in ["calories", "sugar", "protein", "fat"]:
+            match = re.search(rf"(\d+)\s*(?:g\s*)?{label}", nutrition_text)
+            if match:
+                recipe_data["nutrition"][label] = int(match.group(1))
+
+    return recipe_data
+
+@app.route('/', methods=['GET'])
+def home():
+    return jsonify({
+        "name": "Recipe Generation API",
+        "version": "1.0.0",
+        "endpoints": {
+            "/api/healthcheck": "Check API status",
+            "/api/generate": "Generate a recipe",
+            "/api/parse": "Parse a recipe text"
+        }
+    })
+
+@app.route('/api/healthcheck', methods=['GET'])
+def healthcheck():
+    return jsonify({
+        "status": "ok",
+        "message": "Recipe API is running",
+        "timestamp": str(datetime.now())
+    })
+
+@app.route('/api/generate', methods=['POST'])
+def api_generate_recipe():
+    data = request.json
+    if not data or 'goal' not in data:
+        return jsonify({"error": "Missing 'goal' parameter"}), 400
+
+    try:
+        goal = data['goal']
+        max_length = data.get('max_length', 300)
+        temperature = data.get('temperature', 0.7)
+        recipe_text = generate_recipe(goal, max_length, temperature)
+        return jsonify({"goal": goal, "recipe_text": recipe_text})
+    except Exception as e:
+        logger.error(f"Error generating recipe: {str(e)}")
+        return jsonify({"error": str(e)}), 500
+
+@app.route('/api/parse', methods=['POST'])
+def api_parse_recipe():
+    data = request.json
+    if not data or 'recipe_text' not in data:
+        return jsonify({"error": "Missing 'recipe_text' parameter"}), 400
+    try:
+        return jsonify(parse_recipe(data['recipe_text']))
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+    load_model()
+    port = int(os.environ.get('PORT', 7860))
+    app.run(host='0.0.0.0', port=port)

requirements.txt

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+flask==3.1.0
+flask_cors==5.0.1
+torch==2.2.2  # Replace with CPU version or GPU depending on your use
+transformers==4.51.3