Update app.py

app.py

@@ -1,5 +1,5 @@
 # =========================================
-# 1️⃣ Install imports
+# 1️⃣ Imports
 # =========================================
 import os
 import pickle
@@ -10,29 +10,45 @@ from sentence_transformers import SentenceTransformer
 from groq import Groq
 
 # =========================================
-# 2️⃣ Load Groq API from HF Secrets
+# 2️⃣ Groq API
 # =========================================
 client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
 GROQ_MODEL = "llama-3.3-70b-versatile"
 
 # =========================================
-# 3️⃣ Load FAISS + Chunks
+# 3️⃣ Load FAISS + chunks
 # =========================================
 index = faiss.read_index("faiss_index.bin")
 
 with open("chunks.pkl", "rb") as f:
     all_chunks = pickle.load(f)
 
-# Load embedding model (only for query embedding)
+# Embedding model
 embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 
 # =========================================
-# 4️⃣ Groq Query Function
+# 4️⃣ LLM Call Function (MULTILINGUAL)
 # =========================================
 def groq_query(prompt):
     completion = client.chat.completions.create(
         messages=[
-            {"role": "system", "content": "You are a strict university assistant. Answer ONLY using provided context."},
+            {
+                "role": "system",
+                "content": """
+You are a strict university assistant.
+
+RULES:
+- Use ONLY the provided context
+- If answer is not in context, say:
+  "The information is not available in the provided documents. Please check https://uobs.edu.pk/"
+
+LANGUAGE RULES:
+- If user asks in English → reply in English
+- If user asks in Urdu (اردو) → reply in Urdu
+- If user writes in Roman Urdu (e.g. "department ka head kon hai") → reply in Roman Urdu
+- Keep answer clear, correct and simple
+"""
+            },
             {"role": "user", "content": prompt}
         ],
         model=GROQ_MODEL,
@@ -41,28 +57,26 @@ def groq_query(prompt):
     return completion.choices[0].message.content
 
 # =========================================
-# 5️⃣ RAG Function
+# 5️⃣ RAG FUNCTION
 # =========================================
 def rag_answer(query, k=3):
 
     if not query.strip():
         return "Please enter a valid question."
 
+    # Embed query
     query_embedding = embedding_model.encode([query])
     query_embedding = query_embedding / np.linalg.norm(query_embedding, axis=1, keepdims=True)
     query_embedding = query_embedding.astype("float32")
 
+    # Search FAISS
     distances, indices = index.search(query_embedding, k)
     retrieved_texts = [all_chunks[i] for i in indices[0]]
 
     context = "\n\n".join(retrieved_texts)
 
     prompt = f"""
-Use ONLY information below to answer.
-If no answer is found, respond:
-
-"The information is not available in the provided documents. 
-Please check the official University of Baltistan website for more information: https://uobs.edu.pk/"
+Use ONLY the context below to answer the question.
 
 Context:
 {context}
@@ -70,101 +84,84 @@ Context:
 Question:
 {query}
 """
+
     return groq_query(prompt)
 
 # =========================================
-# Gradio UI 
+# 6️⃣ Chat function
 # =========================================
-
-# Sample mock questions
-mock_questions = [
-    "What is the focus of the Botany Department?",
-    "Who is the Head of Chemistry Department?",
-    "What programs does the Computer Science Department offer?",
-    "What environmental challenges does the Earth & Environmental Science Dept address?",
-    "Who is the Head of Mathematics Department?",
-    "What is the goal of Educational Development Department?",
-    "Who leads the Languages and Cultural Studies Department?"
-]
-
-# Function to set mock question into input
-def set_question(q_text):
-    return q_text
-
-# Function to respond via RAG
 def respond(message, history):
+
     if not history:
         history = []
 
-    user_entry = {"role": "user", "content": message}
-    answer = rag_answer(message)  # your RAG function
-    assistant_entry = {"role": "assistant", "content": answer}
+    answer = rag_answer(message)
 
-    history.append(user_entry)
-    history.append(assistant_entry)
+    history.append({"role": "user", "content": message})
+    history.append({"role": "assistant", "content": answer})
 
-    return history, ""  # update chat and clear input
+    return history, ""
 
+# =========================================
+# 7️⃣ Sample Questions
+# =========================================
+mock_questions = [
+    "What is the focus of the Botany Department?",
+    "Who is the Head of Chemistry Department?",
+    "Computer Science department ka head kon hai?",
+    "اردو اور انگلش میں جواب دیں: Education department ka goal kya hai?"
+]
+
+# =========================================
+# 8️⃣ Gradio UI
+# =========================================
 with gr.Blocks() as demo:
-    # App title
+
     gr.Markdown(
-        "<h1 style='text-align:center; color:#2c3e50;'>🎓 UoBs HoDs Insight AI Assistant</h1>"
+        "<h1 style='text-align:center;'>🎓 UoBs Multilingual AI Assistant</h1>"
     )
 
-    # Description
-    gr.Markdown(
-        """
-<div style="text-align:center; color:#34495e; font-size:16px; max-width:900px; margin:auto;">
-Welcome to the <b>UoBs HoDs Insight AI Assistant</b> – your trusted AI-powered guide to the University of Baltistan.  
-This intelligent chatbot delivers accurate and verified information directly from the official messages of all department heads.  
-Ask about academic programs, faculty, research initiatives, or departmental objectives, and receive <b>instant, reliable answers</b> strictly sourced from official university communications.  
-For additional details or to explore further, all information is anchored to the <a href="https://uobs.edu.pk/" target="_blank">official University of Baltistan website</a>.
+    gr.Markdown("""
+<div style="text-align:center;">
+Ask questions in <b>English</b>, <b>Urdu</b>, or <b>Roman Urdu</b>.<br>
+System will automatically respond in the same language.
 </div>
-        """
-    )
+    """)
 
-    # Main layout: Left sidebar + Right chatbot
     with gr.Row():
-        # Left column: mock questions
+
+        # Left panel
         with gr.Column(scale=1):
             gr.Markdown("### 💡 Sample Questions")
-            # create buttons for each question
-            mock_buttons = []
+
             for q in mock_questions:
-                btn = gr.Button(q, elem_classes="mock-btn")
-                mock_buttons.append(btn)
+                btn = gr.Button(q)
+
+                btn.click(
+                    fn=lambda q=q: q,
+                    outputs=None
+                ).then(
+                    lambda q=q: q,
+                    outputs=None
+                )
 
-        # Right column: chatbot + input
+        # Right panel
         with gr.Column(scale=3):
-            chatbot = gr.Chatbot()
-            
-            # Input row: 80% textbox + 20% send button
+
+            chatbot = gr.Chatbot(type="messages")
+
             with gr.Row():
                 user_input = gr.Textbox(
-                    placeholder="Type your question here...",
-                    lines=1,
-                    scale=8  # 80% width
-                )
-                send_btn = gr.Button(
-                    "Send",
-                    variant="primary",
-                    elem_id="send-btn",
-                    scale=2  # 20% width
+                    placeholder="Ask in English, Urdu, or Roman Urdu...",
+                    scale=8
                 )
 
-            # Bind send button to respond function
+                send_btn = gr.Button("Send", scale=2)
+
             send_btn.click(
                 respond,
                 inputs=[user_input, chatbot],
                 outputs=[chatbot, user_input]
             )
 
-    # Bind each mock question button to input field
-    for btn, q in zip(mock_buttons, mock_questions):
-        btn.click(
-            lambda q=q: q,
-            outputs=user_input
-        )
-
-# Launch the app
-demo.launch()
+    demo.launch()