app.py

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

# Load environment variables from .env 


# Retrieve the Hugging Face API token from the environment
# Initialize the InferenceClient (update the model as needed)
client = InferenceClient(
    model="microsoft/Phi-4-mini-reasoning"  # Change to your model if needed
)

# 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; here we use a basic sleep.
            time.sleep(5)
            try:
                # Customize the selector based on 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 ---

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"]
    }
    greet_list = greetings.get(lang, greetings["en"])
    # For languages using Latin script, convert the query to lower case.
    if lang != "am":
        query = query.lower()
    return any(query.startswith(greet) for greet in greet_list)

def generate_dynamic_greeting(language: str) -> str:
    """
    Generate a dynamic, context-relevant greeting using the Hugging Face Inference API.
    """
    system_prompts = {
        "en": (
            "You are a friendly chatbot specializing in agriculture and agro-investment. "
            "A user just greeted you. Generate a warm, dynamic greeting message in English that is context-aware and encourages discussion about agriculture or agro-investment."
        ),
        "fr": (
            "Vous êtes un chatbot chaleureux spécialisé dans l'agriculture et les investissements agroalimentaires. "
            "Un utilisateur vient de vous saluer. Générez un message de salutation dynamique et chaleureux en français, en restant pertinent par rapport à l'agriculture ou aux investissements agroalimentaires."
        ),
        "am": (
            "እርስዎ በግብርናና በአገልግሎት ስርዓተ-ቢዝነስ ውስጥ ባለሙያ ቻትቦት ናቸው። "
            "ተጠቃሚው በአማርኛ ሰላም መልእክት አስቀድመዋል። "
            "በአማርኛ ተዛማጅ እና ትክክለኛ የሆነ ሰላም መልእክት ፍጥረት ያድርጉ።"
        )
    }
    prompt = system_prompts.get(language, system_prompts["en"])
    messages = [{"role": "system", "content": prompt}]
    response = client.chat_completion(
        messages,
        max_tokens=128,
        stream=False,
        temperature=1,
        top_p=0.95,
    )
    try:
        greeting_message = response.choices[0].message.content
    except AttributeError:
        greeting_message = str(response)
    return greeting_message.strip()

def generate_dynamic_out_of_scope_message(language: str) -> str:
    """
    Generate a dynamic out-of-scope message using the Hugging Face Inference API.
    """
    system_prompts = {
        "en": (
            "You are a helpful chatbot specializing in agriculture and agro-investment. "
            "A user just asked a question that is not related to these topics. "
            "Generate a friendly, varied, and intelligent out-of-scope response in English that kindly encourages 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 vient de poser une question qui ne concerne pas ces sujets. "
            "Générez une réponse élégante, variée et intelligente en français pour indiquer que la question est hors de portée, en invitant l'utilisateur à poser une question sur l'agriculture ou les investissements agroalimentaires."
        ),
        "am": (
            "እርስዎ በግብርናና በአገልግሎት ስርዓተ-ቢዝነስ ውስጥ በተለይ የተሞሉ ቻትቦት ናቸው። "
            "ተጠቃሚው ለግብርና ወይም ለአገልግሎት ስርዓተ-ቢዝነስ ተያይዞ ያልሆነ ጥያቄ አስቀድመዋል። "
            "በአማርኛ በተለያዩ መልኩ የውጭ ክፍል መልእክት ፍጥረት ያድርጉ፤ እባኮትን ተጠቃሚውን ለግብርና ወይም ለአገልግሎት ጥያቄዎች ለመጠየቅ ያነጋግሩ።"
        )
    }
    prompt = system_prompts.get(language, system_prompts["en"])
    messages = [{"role": "system", "content": prompt}]
    response = client.chat_completion(
        messages,
        max_tokens=128,
        stream=False,
        temperature=1,
        top_p=0.95,
    )
    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:
    """
    Check if a query relates to agriculture or agro-investment.
    """
    domain_keywords = [
        "agriculture", "farming", "crop", "agro", "investment", "soil",
        "irrigation", "harvest", "organic", "sustainable", "agribusiness",
        "livestock", "agroalimentaire", "agriculture durable",
        "greenhouse", "horticulture", "pesticide", "fertilizer",
        "rural development", "food production", "crop yield", "farm equipment",
        "agronomy", "farming techniques", "organic farming", "agro-tech",
        "farm management", "agrifood"
    ]
    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:
    """
    Retrieve a relevant snippet from the text based on the query.
    """
    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 ---
def respond(message, history: list, system_message, max_tokens, temperature, top_p, language):
    # Check for a greeting.
    if is_greeting(message, language):
        yield generate_dynamic_greeting(language)
        return

    # If query is out of domain, generate an out-of-scope message.
    if not is_domain_query(message):
        yield generate_dynamic_out_of_scope_message(language)
        return

    # Build conversation context from the system message and conversation history.
    messages_list = [{"role": "system", "content": system_message}]
    for user_msg, assistant_msg in history:
        if user_msg:
            messages_list.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages_list.append({"role": "assistant", "content": assistant_msg})
    
    # Optionally add a relevant snippet from the scraped content (if available).
    if knowledge_base:
        snippet = retrieve_relevant_snippet(message, knowledge_base)
        if snippet:
            retrieval_context = f"Reference from Agri Future Investment platform: {snippet}"
            messages_list.insert(0, {"role": "system", "content": retrieval_context})
    
    messages_list.append({"role": "user", "content": message})
    
    # Generate the assistant's answer by streaming responses.
    response_text = ""
    for partial_response in client.chat_completion(
        messages_list,
        max_tokens=1024,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        if partial_response.choices and partial_response.choices[0].delta:
            token = partial_response.choices[0].delta.content
            if token:
                response_text += token
                yield response_text
# --- Gradio Chat Interface ---
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Textbox(
            value="You are AgriFutureBot, a specialized assistant for agriculture and agro-investment insights.", 
            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")
    ],
)

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