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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
import pandas as pd
import csv
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
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()
# 👉 Fee-specific shortcut
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"
"🔗 https://ue.edu.pk/allfeestructure.php"
)
# 🔍 Similarity matching
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 not matched well, log to CSV
if best_score < 0.65:
file_path = "unmatched_queries.csv"
print(f"[DEBUG] Similarity score too low: {best_score}. Logging query to: {file_path}")
# Check if file exists
if not os.path.exists(file_path):
print(f"[DEBUG] File {file_path} does not exist. Creating file with header.")
try:
with open(file_path, mode="w", newline="", encoding="utf-8") as file:
writer = csv.writer(file)
writer.writerow(["Unmatched Queries"])
print(f"[DEBUG] Header written successfully.")
except Exception as e:
print(f"[ERROR] Failed to create file: {e}")
try:
with open(file_path, mode="a", newline="", encoding="utf-8") as file:
writer = csv.writer(file)
writer.writerow([user_input])
print(f"[DEBUG] Query logged: {user_input}")
except Exception as e:
print(f"[ERROR] Failed to write query to CSV: {e}")
# 🧠 Prompt construction
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:"""
# 🧠 Query LLM
llm_response = query_groq_llm(prompt)
# 🧾 Process LLM output
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
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