first function to analyze recipe done

.gitignore

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 .idea/
+.env
+**/__pycache__/**
+test.**
+test_*.py

requirements.txt

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+fastapi
+google
+requests
+dotenv
+mistralai

src/model.py

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+import json
+from typing import Any
+
+from mistralai import Mistral
+
+from .utils import create_message
+
+
+def recipe_analysis(recipe: str, **kwargs) -> dict[str, Any]:
+    """
+    Analyze a recipe returning ingredients and preparation steps
+    Call a MistralAI model with parameters in kwargs
+    :param recipe:
+    :param kwargs:
+    :return:
+    """
+    task = f"""Your task is to analyze a recipe.
+For this analysis you have to extract ingredients and their quantitites.
+The quantities extracted must be expressed in grams (g).
+If any other unit are used, convert it to grams.
+You also have to extract the preparation method.
+Temperatures in the preparation method must be expressed in °Celsius (°C). 
+If Temperatures are in Farhenheit (°F) convert it in °Celsius (°C) and round it to the nearest integer multiple of ten.
+Your answer must be formatted so that it can be stored in a json format.
+The json must be containing the fields: 
+    - "ingredients", that is an array of objects with field "name" that is a string and "amount" that is a number;
+    - "preparation", that is the text of the preparation method.
+
+If the original language of the recipe is not english, translate all the ingredients name and the preparation process to english.
+Here is the recipe you have to analyze
+
+RECIPE:
+{recipe}
+    """
+
+    client = Mistral(api_key=kwargs["api_key"])
+    return json.loads(client.chat.complete(
+        model=kwargs["model"],
+        messages=[create_message("user", task)],
+        response_format={ "type": "json_object" }
+    ).choices[0].message.content)

src/utils.py

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+def create_message(role: str, content: str):
+    return {"role": role, "content": content}