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| 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 | |
| ) | |
| 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_input_lower = user_input.lower().strip() | |
| # 👉 Check if question is about fee | |
| if any(keyword in user_input_lower for keyword in ["semester fee","semester fees"]): | |
| 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" | |
| ) | |
| # 🔁 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() | |
| 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:"]: | |
| if marker in llm_response: | |
| response = llm_response.split(marker)[-1].strip() | |
| break | |
| else: | |
| response = llm_response | |
| else: | |
| response = dataset_answers[best_match_idx] if best_score >= 0.65 else """For official information: | |
| 📞 +92-42-99262231-33 | |
| ✉️ [email protected] | |
| 🌐 ue.edu.pk""" | |
| return response |