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import gradio as gr
import os
import json
import faiss
import numpy as np
import torch
from sentence_transformers import SentenceTransformer
from huggingface_hub import InferenceClient, hf_hub_download
# 🔹 Hugging Face Credentials
HF_REPO = "Futuresony/future_ai_12_10_2024.gguf"
HF_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN') # Ensure this is set in your environment
# 🔹 FAISS Index Path
FAISS_PATH = "asa_faiss.index"
# 🔹 Load Sentence Transformer for Embeddings
embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
# 🔹 Load FAISS Index from Hugging Face
faiss_local_path = hf_hub_download(HF_REPO, "asa_faiss.index", token=HF_TOKEN)
faiss_index = faiss.read_index(faiss_local_path)
# 🔹 Initialize Hugging Face Model Client
client = InferenceClient(model=HF_REPO, token=HF_TOKEN)
# 🔹 Retrieve Relevant FAISS Data
def retrieve_faiss_knowledge(user_query, top_k=3):
query_embedding = embedder.encode([user_query], convert_to_tensor=True).cpu().numpy()
distances, indices = faiss_index.search(query_embedding, top_k)
retrieved_texts = []
for idx in indices[0]: # Extract top_k results
if idx != -1: # Ensure valid index
retrieved_texts.append(f"Example {idx}: (Extracted FAISS Data)")
return "\n".join(retrieved_texts) if retrieved_texts else "**No relevant FAISS data found.**"
# 🔹 Chatbot Response Function (Forcing FAISS Context)
def respond(message, history, system_message, max_tokens, temperature, top_p):
faiss_context = retrieve_faiss_knowledge(message)
# 🔥 Force the model to use FAISS
full_prompt = f"""### System Instruction:
You MUST use the provided FAISS data to generate your response.
If no FAISS data is found, return "I don't have enough information."
### Retrieved FAISS Data:
{faiss_context}
### User Query:
{message}
### Response:
"""
response = client.text_generation(
full_prompt,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
# ✅ Extract only the model-generated response
cleaned_response = response.split("### Response:")[-1].strip()
history.append((message, cleaned_response)) # ✅ Update chat history
yield cleaned_response # ✅ Output the response
# 🔹 Gradio Chat Interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a knowledge assistant that must use FAISS context.", label="System message"),
gr.Slider(minimum=1, maximum=250, value=128, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.9, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.99, step=0.01, label="Top-p (nucleus sampling)"),
],
)
if __name__ == "__main__":
demo.launch()
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