Update rag.py

rag.py

@@ -1,50 +1,16 @@
-import json
-from sentence_transformers import SentenceTransformer, util
-from groq import Groq
-import datetime
-import requests
-from io import BytesIO
-from PIL import Image, ImageDraw, ImageFont
-import numpy as np
-from dotenv import load_dotenv
-import os
-
-# Load environment variables
-load_dotenv()
-
-# Initialize Groq client
-groq_client = Groq(api_key=os.getenv("GROQ_API_KEY"))
-
-# Load models and dataset
-similarity_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
-
-# Load dataset (automatically using the path)
-with open('dataset.json', 'r') as f:
-    dataset = json.load(f)
-
-# Precompute embeddings
-dataset_questions = [item.get("input", "").lower().strip() for item in dataset]
-dataset_answers = [item.get("response", "") for item in dataset]
-dataset_embeddings = similarity_model.encode(dataset_questions, convert_to_tensor=True)
-
-def query_groq_llm(prompt, model_name="llama3-70b-8192"):
-    try:
-        chat_completion = groq_client.chat.completions.create(
-            messages=[{
-                "role": "user",
-                "content": prompt
-            }],
-            model=model_name,
-            temperature=0.7,
-            max_tokens=500
+def get_best_answer(user_input):
+    user_input_lower = user_input.lower().strip()
+
+    # 👉 Check if question is about fee
+    if any(keyword in user_input_lower for keyword in ["fee", "fees", "charges", "semester fee"]):
+        return (
+            "💰 For complete and up-to-date fee details for all programs, "
+            "please visit the official University of Education fee structure page:\n"
+            "🔗 https://ue.edu.pk/allfeestructure.php"
         )
-        return chat_completion.choices[0].message.content.strip()
-    except Exception as e:
-        print(f"Error querying Groq API: {e}")
-        return ""
 
-def get_best_answer(user_input):
-    user_embedding = similarity_model.encode(user_input.lower().strip(), convert_to_tensor=True)
+    # 🔁 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()
     best_score = similarities[best_match_idx].item()
@@ -79,4 +45,4 @@ def get_best_answer(user_input):
         ✉️ info@ue.edu.pk
         🌐 ue.edu.pk"""
     
-    return response
+    return response