app.py

import gradio as gr
import requests
import os
import faiss
import numpy as np
import json
from sentence_transformers import SentenceTransformer

# Load RAG context
with open("texts.json", "r", encoding="utf-8") as f:
    texts = json.load(f)

index = faiss.read_index("faiss_index.bin")
embed_model = SentenceTransformer("all-MiniLM-L6-v2")

API_KEY = os.environ.get("OPENROUTER_API_KEY")
MODEL = "qwen/qwen-2.5-coder-32b-instruct:free"

# 🔍 Get relevant context from vector index
def get_context(query, top_k=5):
    query_vec = embed_model.encode([query])
    D, I = index.search(np.array(query_vec), top_k)
    return "\n".join([texts[i] for i in I[0]])

# 💬 Generate reply with context + chat history
def chat_fn(message, history):
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }

    context = get_context(message)

    system_prompt = f"""You are Codex Assistant by LogIQ Curve — a helpful and humanlike AI.
Avoid robotic language. Respond using the following information:

{context}
"""

    messages = [{"role": "system", "content": system_prompt}]
    for user, assistant in history:
        messages.append({"role": "user", "content": user})
        messages.append({"role": "assistant", "content": assistant})
    messages.append({"role": "user", "content": message})

    payload = {"model": MODEL, "messages": messages}

    try:
        response = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload)
        response.raise_for_status()
        reply = response.json()["choices"][0]["message"]["content"]
    except Exception as e:
        reply = f"❌ Error: {e}"

    return reply

# ✅ API endpoint: plain input/output
def api_respond(message):
    return chat_fn(message, [])

api_interface = gr.Interface(
    fn=api_respond,
    inputs=gr.Textbox(lines=1, placeholder="Ask me anything..."),
    outputs="text",
    live=False
)

# 🎨 Custom CSS and chat UI
custom_css = """
* {
  font-family: 'Segoe UI', sans-serif;
}
#chat-window {
  background: linear-gradient(to bottom right, #f9f9f9, #e0e7ff);
  padding: 20px;
  height: 80vh;
  overflow-y: auto;
  border-radius: 12px;
  box-shadow: inset 0 0 8px rgba(0,0,0,0.05);
}
.message {
  padding: 12px 18px;
  margin: 10px 0;
  border-radius: 18px;
  max-width: 75%;
  word-wrap: break-word;
  box-shadow: 0 4px 14px rgba(0,0,0,0.08);
}
.message.user {
  background-color: #4F46E5;
  color: white;
  align-self: flex-end;
  border-bottom-right-radius: 4px;
}
.message.ai {
  background-color: #ffffff;
  color: #111;
  align-self: flex-start;
  border-bottom-left-radius: 4px;
}
#input-row {
  display: flex;
  padding: 12px;
  background: white;
  border-top: 1px solid #ddd;
}
textarea {
  flex: 1;
  padding: 10px;
  border-radius: 10px;
  border: 1px solid #ccc;
  font-size: 16px;
  resize: none;
}
button {
  margin-left: 10px;
  border-radius: 10px;
  background-color: #4F46E5;
  color: white;
  font-weight: bold;
  padding: 0 20px;
  box-shadow: 0 4px 12px rgba(79, 70, 229, 0.3);
}
"""

with gr.Blocks(css=custom_css) as chatbot_ui:
    chatbot_state = gr.State([])

    with gr.Column():
        chatbox = gr.HTML('<div id="chat-window"></div>', elem_id="chat-window")
        with gr.Row(elem_id="input-row"):
            msg = gr.Textbox(
                placeholder="Type your message...",
                lines=1,
                show_label=False,
                scale=8
            )
            send = gr.Button("Send", scale=1)

    def respond(message, state):
        state_pairs = [(state[i][1], state[i+1][1]) for i in range(0, len(state)-1, 2)]
        response = chat_fn(message, state_pairs)
        state.append(("user", message))
        state.append(("ai", response))

        html = ""
        for role, content in state:
            html += f'<div class="message {role}">{content}</div>'
        return html, state, ""

    send.click(respond, [msg, chatbot_state], [chatbox, chatbot_state, msg])
    msg.submit(respond, [msg, chatbot_state], [chatbox, chatbot_state, msg])

# 🚀 Launch both UI and API
if __name__ == "__main__":
    chatbot_ui.queue().launch(share=True, inline=False)
    api_interface.launch(inline=False)