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app (1).py

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+import gradio as gr
+from rag import get_best_answer
+
+# Custom CSS for the interface
+css = """
+#chatbot { 
+    height: 350px; 
+    overflow: auto; 
+    border-radius: 10px;
+    border: 1px solid #e0e0e0;
+}
+.textbox {
+    border-radius: 20px !important;
+    padding: 12px 20px !important;
+}
+.btn-column {
+    display: flex;
+    flex-direction: column;
+    gap: 10px;
+}
+"""
+
+def create_interface():
+    with gr.Blocks(css=css, theme="soft") as demo:
+        gr.Markdown("""
+            <h1 style='text-align: center;'>University of Education Lahore Chatbot</h1>
+            <p style='text-align: center;'>Official AI Assistant for University Information</p>
+        """)
+
+        # Define the chat interface
+        chatbot = gr.Chatbot(elem_id="chatbot")
+        examples = [
+            "What are the admission requirements?",
+            "How can I contact the administration?",
+            "What programs are offered?"
+        ]
+
+        with gr.Row():
+            message = gr.Textbox(
+                label="Type your question here",
+                placeholder="Ask about admissions, programs, or university services...",
+                elem_classes="textbox",
+                scale=4
+            )
+            with gr.Column(scale=1, elem_classes="btn-column"):
+                submit_button = gr.Button("↩️ Enter")
+                reset_button = gr.Button("🗑️ Reset Chat")
+
+        # Set up both Enter key and button to trigger the response
+        def respond(message, chat_history):
+            bot_message = get_best_answer(message)
+            chat_history.append((message, bot_message))
+            return "", chat_history
+
+        message.submit(respond, [message, chatbot], [message, chatbot])
+        submit_button.click(respond, [message, chatbot], [message, chatbot])
+
+        # Reset button to clear history
+        def reset_conversation():
+            return []
+        
+        reset_button.click(reset_conversation, [], [chatbot])
+
+        gr.Examples(examples, inputs=message)
+
+    return demo
+
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch()

rag.py

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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 ["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"
+        )
+
+    # 🔁 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

requirements (1).txt

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+sentence-transformers
+groq
+gradio
+pillow
+requests
+numpy
+python-dotenv