Update rag.py

rag.py

@@ -7,6 +7,8 @@ from io import BytesIO
 from PIL import Image, ImageDraw, ImageFont
 import numpy as np
 from dotenv import load_dotenv
+from datasets import load_dataset, Dataset
+import pandas as pd
 import os
 
 # Load environment variables
@@ -18,7 +20,16 @@ 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)
+# Constants
+HF_DATASET_REPO = "midrees2806/unmatched_queries"
+HF_TOKEN = os.getenv("HF_TOKEN")
+GREETINGS = [
+    "hi", "hello", "hey", "good morning", "good afternoon", "good evening",
+    "assalam o alaikum", "salam", "aoa", "hi there",
+    "hey there", "greetings"
+]
+
+# Load dataset
 with open('dataset.json', 'r') as f:
     dataset = json.load(f)
 
@@ -27,6 +38,24 @@ 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)
 
+# Save unmatched queries to Hugging Face
+def manage_unmatched_queries(query: str):
+    try:
+        timestamp = datetime.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"Failed to save query: {e}")
+
+# Query Groq LLM
 def query_groq_llm(prompt, model_name="llama3-70b-8192"):
     try:
         chat_completion = groq_client.chat.completions.create(
@@ -43,49 +72,77 @@ def query_groq_llm(prompt, model_name="llama3-70b-8192"):
         print(f"Error querying Groq API: {e}")
         return ""
 
+# 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()
 
-    # 👉 Check if question is about fee
-    if any(keyword in user_input_lower for keyword in ["semester fee","semester fees"]):
+    # 🟡 Greet back if user greets
+    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?"
+
+    # 💰 Fee-specific shortcut
+    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
+    # 🔁 Similarity-based 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 best_score < 0.65:
+        manage_unmatched_queries(user_input)
+
+    # 🧠 Use original dataset answer if matched well
     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:"""
+        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:
+"""
     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:"""
+        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:
+"""
 
     llm_response = query_groq_llm(prompt)
 
     if llm_response:
-        for marker in ["Improved Answer:", "Official Answer:"]:
+        for marker in ["Improved Answer:", "Official Answer:", "Rephrased Answer:"]:
             if marker in llm_response:
-                response = llm_response.split(marker)[-1].strip()
-                break
-        else:
-            response = llm_response
+                return llm_response.split(marker)[-1].strip()
+        return 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
+        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"
+        )