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import gradio as gr
import requests
import os
import faiss
import numpy as np
import json
from sentence_transformers import SentenceTransformer
# ✅ Load context data
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"
# ✅ RAG context retriever
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]])
# ✅ Chat handler
def chat_fn(message, history):
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
context = get_context(message)
messages = [
{"role": "system", "content": f"You are a helpful assistant. ONLY use the context below to answer:\n\n{context}"}
]
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
# ✅ Launch chatbot without title or description
gr.ChatInterface(
fn=chat_fn,
theme="soft"
).launch()
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