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import gradio as gr
from huggingface_hub import InferenceClient
pip install sentence_transformers
from sentence_transformers import SentenceTransformer
import torch
import numpy as np
with open("knowledge.txt" , "r", encoding="utf-8") as f:
knowledge_base = f.read()
print("Knowledge base loaded.")
cleaned_text = knowledge_base.strip()
chunks = cleaned_text.split("\n")
cleaned_chunks = []
for chunk in chunks:
stripped_chunk = chunk.strip()
if stripped_chunk:
cleaned_chunks.append(stripped_chunk)
print(cleaned_chunks)
model = SentenceTransformer('all-MiniLM-L6-v2')
chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)
print(chunk_embeddings)
def get_top_chunks(query):
query_embedding = model.encode(query, convert_to_tensor=True)
query_embedding_normalized = query_embedding / query_embedding.norm()
chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
print(similarities)
top_indices = torch.topk(similarities, k=3).indices
print(top_indices)
top_chunks = []
for i in top_indices:
chunk = chunks[i]
top_chunks.append(chunk)
return top_chunks
client = InferenceClient("google/gemma-3-27b-it")
def respond(message,history):
messages = [{"role": "system" , "content" : "You're a supportive and helpful feminist"}]
if history:
messages.extend(history)
messages.append({"role" : "user", "content" : message})
response = ""
for message in client.chat_completion(
messages,
max_tokens = 150,
stream=True,
):
token = message.choices[0].delta.content
response += token
yield response
print(response)
chatbot = gr.ChatInterface(respond, type = "messages")
chatbot.launch(debug=True)
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