app.py

# app.py

from llama_index.core import VectorStoreIndex, StorageContext, ServiceContext, Document
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.together import TogetherLLM
from llama_index.core import Settings
from qdrant_client import QdrantClient

# === Qdrant Config ===
QDRANT_API_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhY2Nlc3MiOiJtIn0.9Pj8v4ACpX3m5U3SZUrG_jzrjGF-T41J5icZ6EPMxnc"
QDRANT_URL = "https://d36718f0-be68-4040-b276-f1f39bc1aeb9.us-east4-0.gcp.cloud.qdrant.io"
COLLECTION_NAME = "demo-chatbot"

# === Embedding & LLM Setup ===
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")

llm = TogetherLLM(
    model="meta-llama/Llama-3-8b-chat-hf",
    api_key="a36246d65d8290f43667350b364c5b6bb8562eb50a4b947eec5bd7e79f2dffc6",
    temperature=0.3,
    max_tokens=1024,
    top_p=0.7
)

Settings.llm = llm
Settings.embed_model = embed_model

# === Qdrant Integration ===
qdrant_client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)

vector_store = QdrantVectorStore(
    client=qdrant_client,
    collection_name=COLLECTION_NAME
)

# === Build Index ===
index = VectorStoreIndex.from_vector_store(vector_store)
query_engine = index.as_query_engine(similarity_top_k=5)

# === Enhanced RAG Chain with References ===
def rag_chain(query: str, include_sources: bool = True) -> str:
    response = query_engine.query(query)
    response_text = str(response)

    if include_sources:
        references = get_clickable_references_from_response(response)
        if references:
            response_text += "\n\n🔗 **Sources:**\n" + "\n".join(references)

    return response_text

# === Clickable Reference Links (top-2 from response nodes) ===
def get_clickable_references_from_response(response, max_refs: int = 2):
    seen = set()
    links = []
    for node in response.source_nodes:
        metadata = node.node.metadata
        section = metadata.get("section", "Unknown")
        source = metadata.get("source", "Unknown")
        key = (section, source)
        if key not in seen:
            seen.add(key)
            if source.startswith("http"):
                links.append(f"- [{section}]({source})")
            else:
                links.append(f"- {section}: {source}")
        if len(links) >= max_refs:
            break
    return links

from datetime import datetime
import time
import gradio as gr

# Chat handler
def chat_interface(message, history):
    history = history or []

    message = message.strip()
    if not message:
        raise ValueError("Please enter a valid question.")

    timestamp_user = datetime.now().strftime("%H:%M:%S")
    user_msg = f"🧑 **You**\n{message}\n\n⏱️ {timestamp_user}"
    bot_msg = "⏳ _Bot is typing..._"
    history.append((user_msg, bot_msg))

    try:
        time.sleep(0.5)
        answer = rag_chain(message)  # already includes references
        full_response = answer.strip()

        timestamp_bot = datetime.now().strftime("%H:%M:%S")
        bot_msg = f"🤖 **Bot**\n{full_response}\n\n⏱️ {timestamp_bot}"
        history[-1] = (user_msg, bot_msg)

    except Exception as e:
        timestamp_bot = datetime.now().strftime("%H:%M:%S")
        error_msg = f"🤖 **Bot**\n⚠️ {str(e)}\n\n⏱️ {timestamp_bot}"
        history[-1] = (user_msg, error_msg)

    return history, history, ""

# Gradio UI
def launch_gradio():
    with gr.Blocks(css="""
    .gr-button {
        background-color: orange !important;
        color: white !important;
        font-weight: bold;
        border-radius: 6px !important;
        border: 1px solid darkorange !important;
    }

    .gr-button:hover {
        background-color: darkorange !important;
    }

    .gr-textbox textarea {
        border: 2px solid orange !important;
        border-radius: 6px !important;
        padding: 0.75rem !important;
        font-size: 1rem;
    }
    """) as demo:

        gr.Markdown("# 💬 ImageOnline RAG Chatbot")
        gr.Markdown("Welcome! Ask about Website Designing, Web Development, App Development, About Us, Digital Marketing etc.")

        chatbot = gr.Chatbot()
        state = gr.State([])

        with gr.Row(equal_height=True):
            msg = gr.Textbox(
                placeholder="Ask your question here...",
                show_label=False,
                scale=9
            )
            send_btn = gr.Button("🚀 Send", scale=1)

        msg.submit(chat_interface, inputs=[msg, state], outputs=[chatbot, state, msg])
        send_btn.click(chat_interface, inputs=[msg, state], outputs=[chatbot, state, msg])

        with gr.Row():
            clear_btn = gr.Button("🧹 Clear Chat")
            clear_btn.click(fn=lambda: ([], []), outputs=[chatbot, state])

    return demo

# Launch
demo = launch_gradio()
demo.launch()