Update app.py

app.py

@@ -23,10 +23,10 @@ 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:
@@ -43,135 +43,66 @@ 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 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።"
-        ]
+# Rather than using fixed out-of-scope messages, use the model via Hugging Face to generate them.
+def generate_dynamic_out_of_scope_message(language: str) -> str:
+    # Define language-specific system prompts for generating a dynamic out-of-scope message.
+    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}]
+
+    # Call the model without streaming to generate the complete message.
+    response = client.chat_completion(
+        messages,
+        max_tokens=80,
+        stream=False,
+        temperature=0.7,
+        top_p=0.95,
+    )
+    # Depending on the client structure, adjust the extraction of the generated text.
+    try:
+        out_message = response.choices[0].message.content
+    except AttributeError:
+        # If the response structure differs, do a fallback conversion.
+        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).
+# A helper to determine domain relevance (basic implementation; 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
+        "livestock", "agroalimentaire", "agriculture durable"
     ]
     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()
+    for sentence in sentences12