Update rag.py

rag.py

@@ -1,13 +1,20 @@
 import json
-from sentence_transformers import SentenceTransformer, util
-from groq import Groq
-from datetime import datetime
+import glob
 import os
 import pandas as pd
-from datasets import load_dataset, Dataset
+from datetime import datetime
 from dotenv import load_dotenv
-import random
-import glob
+
+# AI and Data Libraries
+from sentence_transformers import SentenceTransformer, util
+from groq import Groq
+from datasets import load_dataset, Dataset
+
+# Image and Utility Libraries
+import requests
+from io import BytesIO
+from PIL import Image, ImageDraw, ImageFont
+import numpy as np
 
 # Load environment variables
 load_dotenv()
@@ -15,31 +22,17 @@ load_dotenv()
 # Initialize Groq client
 groq_client = Groq(api_key=os.getenv("GROQ_API_KEY"))
 
-# Load similarity model
+# Load models and dataset
 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
+# Load multiple JSON datasets from the 'datasets' folder
 dataset = []
 try:
+    # Using glob to find all json files in the folder
     json_files = glob.glob('datasets/*.json')
     for file_path in json_files:
         with open(file_path, 'r', encoding='utf-8') as f:
@@ -48,17 +41,20 @@ try:
                 for item in data:
                     if isinstance(item, dict) and 'Question' in item and 'Answer' in item:
                         dataset.append(item)
-                    else:
-                        print(f"Invalid entry in {file_path}: {item}")
             else:
-                print(f"File {file_path} does not contain a list.")
+                print(f"Skipping {file_path}: File does not contain a list.")
 except Exception as e:
     print(f"Error loading datasets: {e}")
 
 # Precompute embeddings
 dataset_questions = [item.get("Question", "").lower().strip() for item in dataset]
 dataset_answers = [item.get("Answer", "") for item in dataset]
-dataset_embeddings = similarity_model.encode(dataset_questions, convert_to_tensor=True)
+
+if dataset_questions:
+    dataset_embeddings = similarity_model.encode(dataset_questions, convert_to_tensor=True)
+else:
+    dataset_embeddings = None
+    print("Warning: No data found in the datasets folder.")
 
 # Save unmatched queries to Hugging Face
 def manage_unmatched_queries(query: str):
@@ -69,6 +65,7 @@ def manage_unmatched_queries(query: str):
             df = ds["train"].to_pandas()
         except:
             df = pd.DataFrame(columns=["Query", "Timestamp", "Processed"])
+            
         if query not in df["Query"].values:
             new_entry = {"Query": query, "Timestamp": timestamp, "Processed": False}
             df = pd.concat([df, pd.DataFrame([new_entry])], ignore_index=True)
@@ -94,23 +91,25 @@ 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
+# Main logic function
 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 and not any(greet in user_input_lower for greet in GREETINGS):
-        return "Please ask your question properly with at least 3 words."
-
-    if any(keyword in user_input_lower for keyword in ["fee structure", "fees structure", "semester fees", "semester fee"]):
+    # 👉 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 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"
+            "You’ll find comprehensive information regarding tuition, admission charges, and other applicable fees there.\n"
             "🔗 https://ue.edu.pk/allfeestructure.php"
         )
 
+    # 🔁 Continue with normal similarity-based logic
+    if dataset_embeddings is None:
+        return "I am currently updating my database. Please try again in a moment."
+
     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()
@@ -118,26 +117,33 @@ def get_best_answer(user_input):
 
     if best_score < 0.65:
         manage_unmatched_queries(user_input)
-        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:
-"""
+
+    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:"""
 
     llm_response = query_groq_llm(prompt)
 
     if llm_response:
-        for marker in ["Improved Answer:", "Official Answer:", "Rephrased Answer:"]:
+        for marker in ["Improved Answer:", "Official Answer:"]:
             if marker in llm_response:
-                return llm_response.split(marker)[-1].strip()
-        return llm_response
+                response = llm_response.split(marker)[-1].strip()
+                break
+        else:
+            response = llm_response
     else:
-        return dataset_answers[best_match_idx]
+        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