Update rag.py

rag.py

@@ -1,148 +1,91 @@
-import os
 import json
-import requests
-import pandas as pd
-from dotenv import load_dotenv
-from datetime import datetime
 from sentence_transformers import SentenceTransformer, util
 from groq import Groq
-from datasets import load_dataset, Dataset
+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 from .env
+# Load environment variables
 load_dotenv()
 
-# ✅ API Keys
-HF_TOKEN = os.getenv("HF_TOKEN")
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+# Initialize Groq client
+groq_client = Groq(api_key=os.getenv("GROQ_API_KEY"))
 
-# ✅ Initialize Groq client
-groq_client = Groq(api_key=GROQ_API_KEY)
-
-# ✅ Hugging Face Dataset Repo
-HF_DATASET_REPO = "midrees2806/unmatched_queries"
-
-# ✅ Sentence Transformer model for semantic similarity
+# Load models and dataset
 similarity_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
 
-# ✅ Greeting keywords
-GREETINGS = [
-    "hi", "hello", "hey", "good morning", "good afternoon", "good evening",
-    "assalam o alaikum", "salam", "aoa", "hi there", "hey there", "greetings"
-]
-
-# ✅ Load dataset
-try:
-    with open('dataset.json', 'r') as f:
-        dataset = json.load(f)
-    assert all('input' in d and 'response' in d for d in dataset), "Invalid dataset format"
-except Exception as e:
-    print(f"[ERROR] Loading dataset: {e}")
-    dataset = []
+# Load dataset (automatically using the path)
+with open('dataset.json', 'r') as f:
+    dataset = json.load(f)
 
-# ✅ Prepare embeddings
-dataset_questions = [d["input"].lower().strip() for d in dataset]
-dataset_answers = [d["response"] for d in dataset]
+# 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)
 
-# ✅ Function: Save unmatched queries to Hugging Face Hub
-def manage_unmatched_queries(query: str):
-    try:
-        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        try:
-            ds = load_dataset(HF_DATASET_REPO, token=HF_TOKEN)
-            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)
-            updated_ds = Dataset.from_pandas(df)
-            updated_ds.push_to_hub(HF_DATASET_REPO, token=HF_TOKEN)
-    except Exception as e:
-        print(f"[ERROR] Logging unmatched query: {e}")
-
-# ✅ Function: Call Groq LLM
-def query_groq_llm(prompt: str, model_name="llama3-70b-8192") -> str:
+def query_groq_llm(prompt, model_name="llama3-70b-8192"):
     try:
-        completion = groq_client.chat.completions.create(
-            messages=[{"role": "user", "content": prompt}],
+        chat_completion = groq_client.chat.completions.create(
+            messages=[{
+                "role": "user",
+                "content": prompt
+            }],
             model=model_name,
             temperature=0.7,
             max_tokens=500
         )
-        return completion.choices[0].message.content.strip()
+        return chat_completion.choices[0].message.content.strip()
     except Exception as e:
-        print(f"[ERROR] Groq LLM call failed: {e}")
+        print(f"Error querying Groq API: {e}")
         return ""
 
-# ✅ Main RAG logic
 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 any(greet in user_input_lower for greet in GREETINGS):
-        greeting_response = query_groq_llm(
-            f"You are an official assistant for University of Education Lahore. "
-            f"Respond to this greeting in a friendly and professional manner: {user_input}"
-        )
-        return greeting_response if greeting_response else "Hello! How can I assist you today?"
-
-    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
     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:
-        manage_unmatched_queries(user_input)
-
     if best_score >= 0.65:
         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:
-"""
+        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"""Name is UOE AI Assistant! 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:
-"""
+        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] if best_score >= 0.65 else (
-            "For official information:\n"
-            "📞 +92-42-99262231-33\n"
-            "✉️ info@ue.edu.pk\n"
-            "🌐 https://ue.edu.pk"
-        )
+        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