app.py

import random
import re
from huggingface_hub import InferenceClient

# Initialize the InferenceClient with your Hugging Face API token
client = InferenceClient(
    model="HuggingFaceH4/zephyr-7b-beta",  # Specify your model here
    token="your_huggingface_api_token"     # Replace with your actual token
)

# Multilingual greetings dictionary
greetings = {
    "en": ["hello", "hi", "hey", "good morning", "good afternoon", "good evening"],
    "fr": ["bonjour", "salut", "coucou", "bonsoir"],
    "am": ["ሰላም", "ሰላም እንደምን", "እንዴት"]
}

def is_greeting(query: str, lang: str) -> bool:
    """
    Check if the user's query is a greeting in the specified language.
    """
    greet_list = greetings.get(lang, greetings["en"])
    # Convert to lowercase for non-Amharic languages
    if lang != "am":
        query = query.lower()
    return any(query.startswith(greet) for greet in greet_list)

def generate_dynamic_out_of_scope_message(language: str) -> str:
    """
    Generate a dynamic out-of-scope message using the Hugging Face Inference API.
    """
    # Define language-specific system prompts
    system_prompts = {
        "en": (
            "You are a helpful chatbot specializing in agriculture and agro-investment. "
            "A user has asked a question unrelated to these topics. "
            "Generate a friendly and intelligent out-of-scope response in English, encouraging the user to ask about agriculture or agro-investment."
        ),
        "fr": (
            "Vous êtes un chatbot utile spécialisé dans l'agriculture et les investissements agroalimentaires. "
            "Un utilisateur a posé une question sans rapport avec ces sujets. "
            "Générez une réponse amicale et intelligente en français, encourageant l'utilisateur à poser des questions sur l'agriculture ou les investissements agroalimentaires."
        ),
        "am": (
            "እርስዎ በግብርናና በአገልግሎት ስርዓተ-ቢዝነስ ውስጥ የሚሰራ እገዛ የሚሰጥ ቻትቦት ነው። "
            "ተጠቃሚው ከእነዚህ ጉዳዮች ውጪ ጥያቄ አቀርቧል። "
            "በአማርኛ የተሰጠ የውጭ ክፍል ምላሽ ይፍጠሩ፣ ተጠቃሚውን ለግብርና ወይም ለአገልግሎት ስርዓተ-ቢዝነስ ጥያቄዎች ለመጠየቅ ያበረታታ።"
        )
    }
    prompt = system_prompts.get(language, system_prompts["en"])
    messages = [{"role": "system", "content": prompt}]

    # Call the model to generate the response
    response = client.chat_completion(
        messages,
        max_tokens=80,
        temperature=0.7,
        top_p=0.95,
    )
    # Extract the generated message content
    try:
        out_message = response.choices[0].message.content
    except AttributeError:
        out_message = str(response)
    return out_message.strip()

def is_domain_query(query: str) -> bool:
    """
    Determine if the query is related to agriculture or agro-investment.
    """
    domain_keywords = [
        "agriculture", "farming", "crop", "agro", "investment", "soil",
        "irrigation", "harvest", "organic", "sustainable", "agribusiness",
        "livestock", "agroalimentaire", "agriculture durable"
    ]
    return any(re.search(r"\b" + keyword + r"\b", query, re.IGNORECASE) for keyword in domain_keywords)

def handle_user_query(query: str, lang: str = "en") -> str:
    """
    Process the user's query and provide an appropriate response.
    """
    if is_greeting(query, lang):
        return random.choice(greetings.get(lang, greetings["en"])).capitalize() + "!"
    elif is_domain_query(query):
        # Here you would integrate your domain-specific response generation
        return "This is a domain-specific question. Processing accordingly..."
    else:
        return generate_dynamic_out_of_scope_message(lang)

# Example usage
user_query = "Tell me about space travel."
response = handle_user_query(user_query, lang="en")
print(response)