app.py

import re
import time
import random
import gradio as gr
from huggingface_hub import InferenceClient

# Optional: Enable scraping if your site is deployed.
ENABLE_SCRAPING = False
SITE_URL = "https://your-agri-future-site.com"

# Global variable to hold scraped content
knowledge_base = ""

# --- Optional: Scraping Functionality ---
if ENABLE_SCRAPING:
    try:
        from selenium import webdriver
        from selenium.webdriver.chrome.options import Options
        from selenium.webdriver.common.by import By

        def scrape_site(url):
            options = Options()
            options.headless = True  # Run browser in headless mode.
            driver = webdriver.Chrome(options=options)
            driver.get(url)
            # Use explicit waits in production code; here we use a simple sleep.
            time.sleep(5)
            try:
                # Customize the selector as per your site's HTML structure.
                content_element = driver.find_element(By.ID, "content")
                page_text = content_element.text
            except Exception as e:
                page_text = "Error encountered during scraping: " + str(e)
            driver.quit()
            return page_text

        knowledge_base = scrape_site(SITE_URL)
        print("Scraped knowledge base successfully.")
    except Exception as e:
        print("Scraping failed or Selenium is not configured:", e)
else:
    print("Scraping is disabled; proceeding without scraped site content.")

# --- Multilingual Helpers ---

# Language-specific greeting detection
def is_greeting(query: str, lang: str) -> bool:
    greetings = {
        "en": ["hello", "hi", "hey", "good morning", "good afternoon", "good evening"],
        "fr": ["bonjour", "salut", "coucou", "bonsoir"],
        "am": ["ሰላም", "ሰላም እንደምን", "እንዴት"]
    }
    # Retrieve greetings for the provided language; default to English if unavailable.
    greet_list = greetings.get(lang, greetings["en"])
    # For Amharic, no transformation; for Latin scripts, convert to lower case.
    if lang != "am":
        query = query.lower()
    return any(query.startswith(greet) for greet in greet_list)

# Language-specific out-of-scope messages
def get_out_of_scope_message(lang: str) -> str:
    messages = {
        "en": [
            "I appreciate your curiosity. However, my expertise lies exclusively in agricultural and agro-investment insights. Could you please frame your question accordingly?",
            "That’s an interesting thought, but I'm tailored specifically for topics concerning agriculture and agro-investment. Please ask a question within that realm.",
            "While I value your inquiry, I'm optimized to provide insights solely on agriculture and related investment matters. Could you rephrase your query to align with these topics?",
            "It appears your question may not be directly tied to agriculture or agro-investment. Please ask something along those lines so I can assist effectively."
        ],
        "fr": [
            "J'apprécie votre curiosité. Cependant, mon expertise se limite exclusivement aux informations sur l'agriculture et les investissements agroalimentaires. Pourriez-vous reformuler votre question en ce sens ?",
            "C'est une pensée intéressante, mais je suis spécialisé dans les domaines de l'agriculture et des investissements agroalimentaires. Merci de poser une question dans ce domaine.",
            "Bien que votre question soit pertinente, je me concentre uniquement sur l'agriculture et les investissements associés. Pourriez-vous reformuler votre demande en conséquence ?",
            "Votre interrogation semble éloignée de l'agriculture ou des investissements agroalimentaires. Merci de poser une question dans ces domaines pour que je puisse vous aider efficacement."
        ],
        "am": [
            "እባክዎ ልጠይቁት ጥያቄ በተለይ በግብርናና በአገልግሎት ስርዓተ-ቢዝነስ ዙሪያ መሆኑን አላስቀምጥም። እባኮትን ጥያቄዎን እንደዚህ በማቅረብ ደግሞ ይሞክሩ።",
            "ልዩ ጥያቄዎችን ማቅረብ ይፈልጋሉ እንጂ፣ እኔ በተለይ በግብርናና በአገልግሎት ስርዓተ-ቢዝነስ ጥያቄዎች ላይ ብቻ እንደሚሰራ ተዘጋጅቻለሁ። እባክዎ ጥያቄዎን በእነዚህ ክስተቶች ውስጥ ያቅርቡ።",
            "እንደምታዩት ጥያቄዎ በግብርና ወይም በአገልግሎት ስርዓተ-ቢዝነስ ላይ የተመረጠ አይደለም። እባክዎ በዚህ አውድ የሆነ ጥያቄ ይጠይቁ።"
        ]
    }
    # Return a random message for the given language; default to English if not available.
    return random.choice(messages.get(lang, messages["en"]))

# Helper to determine if a query is relevant to our domain (English check only; can be expanded).
def is_domain_query(query: str) -> bool:
    domain_keywords = [
        "agriculture", "farming", "crop", "agro", "investment", "soil",
        "irrigation", "harvest", "organic", "sustainable", "agribusiness",
        "livestock",  # additional English keywords
        "agriculture", "agroalimentaire", "agriculture durable"  # French terms can also be included
    ]
    return any(re.search(r"\b" + keyword + r"\b", query, re.IGNORECASE) for keyword in domain_keywords)

def retrieve_relevant_snippet(query: str, text: str, max_length: int = 300) -> str:
    """
    A simple retrieval function that searches for sentences in the text
    containing domain keywords from the query.
    Returns a snippet limited to max_length characters.
    """
    sentences = re.split(r'[.?!]', text)
    for sentence in sentences:
        if is_domain_query(sentence) and all(word.lower() in sentence.lower() for word in query.split()):
            snippet = sentence.strip()
            return snippet[:max_length] + "..." if len(snippet) > max_length else snippet
    return ""

# --- Chat Assistant Response Function ---
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, language):
    # language is expected as a string code: "en", "fr", or "am"

    # Check for a greeting in the appropriate language.
    if is_greeting(message, language):
        greetings = {
            "en": "Hello! How can I assist you today with your agriculture or agro-investment inquiries?",
            "fr": "Bonjour! Comment puis-je vous aider aujourd'hui en matière d'agriculture ou d'investissements agroalimentaires?",
            "am": "ሰላም! ዛሬ ስለ ግብርና ወይም ስለ አገልግሎት ስርዓተ-ቢዝነስ ጥያቄዎች እንዴት ልረዳዎት?"
        }
        yield greetings.get(language, greetings["en"])
        return

    # If the query is not recognized as domain related, return an out-of-scope message.
    if not is_domain_query(message):
        yield get_out_of_scope_message(language)
        return

    # Build conversation context starting with the system message.
    messages_context = [{"role": "system", "content": system_message}]
    for user_msg, assistant_msg in history:
        if user_msg:
            messages_context.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages_context.append({"role": "assistant", "content": assistant_msg})

    # Optional: Append retrieved context from scraped site content.
    if knowledge_base:
        snippet = retrieve_relevant_snippet(message, knowledge_base)
        if snippet:
            retrieval_context = f"Reference info from Agri Future Investment platform: {snippet}"
            messages_context.insert(0, {"role": "system", "content": retrieval_context})

    # Append the new user message.
    messages_context.append({"role": "user", "content": message})

    # Stream the model's reply token-by-token.
    response = ""
    for message_resp in client.chat_completion(
        messages_context,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message_resp.choices[0].delta.content
        response += token
        yield response

# --- Gradio Chat Interface ---

# The language selection dropdown uses language codes: "en" for English, "fr" for French, "am" for Amharic.
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(
            value="You are AgriFutureBot, designed to help visitors of the Agri Future Investment platform understand content about the site and answer questions strictly related to agriculture and agro-investment topics.",
            label="System Message"
        ),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (Nucleus Sampling)"),
        gr.Dropdown(choices=["en", "fr", "am"], value="en", label="Language (en, fr, am)")
    ],
)

if __name__ == "__main__":
    demo.launch()