Update rag.py

rag.py

@@ -2,14 +2,12 @@ import json
 from sentence_transformers import SentenceTransformer, util
 from groq import Groq
 from datetime import datetime
-import requests
-from datasets import load_dataset, Dataset
-from io import BytesIO
-from PIL import Image, ImageDraw, ImageFont
-import numpy as np
-from dotenv import load_dotenv
 import os
 import pandas as pd
+from datasets import load_dataset, Dataset
+from dotenv import load_dotenv
+import random
+import glob
 
 # Load environment variables
 load_dotenv()
@@ -17,13 +15,28 @@ load_dotenv()
 # Initialize Groq client
 groq_client = Groq(api_key=os.getenv("GROQ_API_KEY"))
 
-# Load models and dataset
+# Load similarity model
 similarity_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
 
 # Config
 HF_DATASET_REPO = "midrees2806/unmatched_queries"
 HF_TOKEN = os.getenv("HF_TOKEN")
 
+# Greeting list
+GREETINGS = [
+    "hi", "hello", "hey", "good morning", "good afternoon", "good evening",
+    "assalam o alaikum", "salam", "aoa", "hi there",
+    "hey there", "greetings"
+]
+
+# Fixed rephrased unmatched query responses
+UNMATCHED_RESPONSES = [
+    "Thank you for your query. We’ve forwarded it to our support team and it will be added soon. In the meantime, you can visit the University of Education official website or reach out via the contact details below.\n\n📞 +92-42-99262231-33\n✉️ info@ue.edu.pk\n🌐 https://ue.edu.pk",
+    "We’ve noted your question and it’s in queue for inclusion. For now, please check the University of Education website or contact the administration directly.\n\n📞 +92-42-99262231-33\n✉️ info@ue.edu.pk\n🌐 https://ue.edu.pk",
+    "Your query has been recorded. We’ll update the system with relevant information shortly. Meanwhile, you can visit UE's official site or reach out using the details below:\n\n📞 +92-42-99262231-33\n✉️ info@ue.edu.pk\n🌐 https://ue.edu.pk",
+    "We appreciate your question. It has been forwarded for further processing. Until it’s available here, feel free to visit the official UE website or use the contact options:\n\n📞 +92-42-99262231-33\n✉️ info@ue.edu.pk\n🌐 https://ue.edu.pk"
+]
+
 # Load multiple JSON datasets
 dataset = []
 try:
@@ -64,6 +77,7 @@ def manage_unmatched_queries(query: str):
     except Exception as e:
         print(f"Failed to save query: {e}")
 
+# Query Groq LLM
 def query_groq_llm(prompt, model_name="llama3-70b-8192"):
     try:
         chat_completion = groq_client.chat.completions.create(
@@ -80,24 +94,23 @@ def query_groq_llm(prompt, model_name="llama3-70b-8192"):
         print(f"Error querying Groq API: {e}")
         return ""
 
+# Main logic function to be called from Gradio
 def get_best_answer(user_input):
-
     if not user_input.strip():
         return "Please enter a valid question."
+
     user_input_lower = user_input.lower().strip()
 
-    if len(user_input_lower.split()) < 3:
+    if len(user_input_lower.split()) < 3 and not any(greet in user_input_lower for greet in GREETINGS):
         return "Please ask your question properly with at least 3 words."
 
-    # 👉 Check if question is about fee
-    if any(keyword in user_input_lower for keyword in ["fee structure", "fees structure"]):
+    if any(keyword in user_input_lower for keyword in ["fee structure", "fees structure", "semester fees", "semester fee"]):
         return (
-            "💰 For complete and up-to-date fee details for this program, we recommend visiting the official University of Education fee structure page.\n"
-            "You’ll find comprehensive information regarding tuition, admission charges, and other applicable fees there.\n"
-            "🔗 https://ue.edu.pk/allfeestructure.php"
+            "💰 For the most complete and up-to-date fee details for your program at the University of Education Lahore, please visit the official fee structure page.\n"
+            "This webpage offers a detailed overview of the fee structure, providing you with essential information to support your academic journey at our institution.\n"
+            "🔗 https://drive.google.com/file/d/1B30FKoP6GrkS9pQk10PWKCwcjco5E9Cc/view"
         )
 
-    # 🔁 Continue with normal similarity-based logic
     user_embedding = similarity_model.encode(user_input_lower, convert_to_tensor=True)
     similarities = util.pytorch_cos_sim(user_embedding, dataset_embeddings)[0]
     best_match_idx = similarities.argmax().item()
@@ -105,33 +118,26 @@ def get_best_answer(user_input):
 
     if best_score < 0.65:
         manage_unmatched_queries(user_input)
-    
-    if best_score >= 0.65:
-        original_answer = dataset_answers[best_match_idx]
-        prompt = f"""As an official assistant for University of Education Lahore, provide a clear response:
-        Question: {user_input}
-        Original Answer: {original_answer}
-        Improved Answer:"""
-    else:
-        prompt = f"""As an official assistant for University of Education Lahore, provide a helpful response:
-        Include relevant details about university policies.
-        If unsure, direct to official channels.
-        Question: {user_input}
-        Official Answer:"""
+        return random.choice(UNMATCHED_RESPONSES)
+
+    original_answer = dataset_answers[best_match_idx]
+    prompt = f"""Name is UOE AI Assistant! You are an official assistant for the University of Education Lahore.
+Rephrase the following official answer clearly and professionally. 
+Use structured formatting (like headings, bullet points, or numbered lists) where appropriate.
+DO NOT add any new or extra information. ONLY rephrase and improve the clarity and formatting of the original answer.
+### Question:
+{user_input}
+### Original Answer:
+{original_answer}
+### Rephrased Answer:
+"""
 
     llm_response = query_groq_llm(prompt)
 
     if llm_response:
-        for marker in ["Improved Answer:", "Official Answer:"]:
+        for marker in ["Improved Answer:", "Official Answer:", "Rephrased Answer:"]:
             if marker in llm_response:
-                response = llm_response.split(marker)[-1].strip()
-                break
-        else:
-            response = llm_response
+                return llm_response.split(marker)[-1].strip()
+        return llm_response
     else:
-        response = dataset_answers[best_match_idx] if best_score >= 0.65 else """For official information:
-        📞 +92-42-99262231-33
-        ✉️ info@ue.edu.pk
-        🌐 ue.edu.pk"""
-
-    return response
+        return dataset_answers[best_match_idx]