Update app.py

app.py

@@ -4,11 +4,17 @@ import random
 import gradio as gr
 from huggingface_hub import InferenceClient
 
+# Initialize the InferenceClient (update the model and token as needed)
+client = InferenceClient(
+    model="HuggingFaceH4/zephyr-7b-beta",  # Change to your model if needed
+    token="your_huggingface_api_token"       # Replace with your Hugging Face API token
+)
+
 # 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
+# Global variable to hold scraped content.
 knowledge_base = ""
 
 # --- Optional: Scraping Functionality ---
@@ -23,10 +29,10 @@ if ENABLE_SCRAPING:
             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.
+            # Use explicit waits in production; here we use a basic sleep.
             time.sleep(5)
             try:
-                # Customize the selector as per your site's HTML structure.
+                # 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:
@@ -43,59 +49,106 @@ else:
 
 # --- 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.
+    # 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)
 
-# 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።"
-        ]
+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=80,
+        stream=False,
+        temperature=0.7,
+        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።"
+        )
     }
-    # Return a random message for the given language; default to English if not available.
-    return random.choice(messages.get(lang, messages["en"]))
+    prompt = system_prompts.get(language, system_prompts["en"])
+    messages = [{"role": "system", "content": prompt}]
+    response = client.chat_completion(
+        messages,
+        max_tokens=80,
+        stream=False,
+        temperature=0.7,
+        top_p=0.95,
+    )
+    try:
+        out_message = response.choices[0].message.content
+    except AttributeError:
+        out_message = str(response)
+    return out_message.strip()
 
-# Helper to determine if a query is relevant to our domain (English check only; can be expanded).
 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",  # additional English keywords
-        "agriculture", "agroalimentaire", "agriculture durable"  # French terms can also be included
+        "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:
     """
-    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.
+    Retrieve a relevant snippet from the text based on the query.
     """
     sentences = re.split(r'[.?!]', text)
     for sentence in sentences:
@@ -105,71 +158,59 @@ def retrieve_relevant_snippet(query: str, text: str, max_length: int = 300) -> s
     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.
+def respond(message, history: list, system_message, max_tokens, temperature, top_p, language):
+    # Check for a greeting.
     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"])
+        yield generate_dynamic_greeting(language)
         return
 
-    # If the query is not recognized as domain related, return an out-of-scope message.
+    # If query is out of domain, generate an out-of-scope message.
     if not is_domain_query(message):
-        yield get_out_of_scope_message(language)
+        yield generate_dynamic_out_of_scope_message(language)
         return
 
-    # Build conversation context starting with the system message.
-    messages_context = [{"role": "system", "content": system_message}]
+    # 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_context.append({"role": "user", "content": user_msg})
+            messages_list.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.
+            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 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,
+            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=max_tokens,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        token = message_resp.choices[0].delta.content
-        response += token
-        yield response
+        token = partial_response.choices[0].delta.content
+        response_text += token
+        yield response_text
 
 # --- Gradio Chat Interface ---
-
-# The language selection dropdown uses language codes: "en" for English, "fr" for French, "am" for Amharic.
 demo = gr.ChatInterface(
-    respond,
+    fn=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.",
+            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 (en, fr, am)")
+        gr.Dropdown(choices=["en", "fr", "am"], value="en", label="Language")
     ],
 )