app.py

import gradio as gr
# from transformers import pipeline
# from transformers.utils import logging
from huggingface_hub import InferenceClient
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core.vector_stores import (MetadataFilters, ExactMatchFilter, )
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
import torch
from llama_index.core import (
    VectorStoreIndex,
    Document,
    Settings,
)
from llama_index.llms.huggingface import (HuggingFaceLLM, )
from llama_index.llms.huggingface_api import (HuggingFaceInferenceAPI, )
from llama_index.core.base.llms.types import ChatMessage
from huggingface_hub import login
import chromadb as chromadb
from chromadb.utils import embedding_functions
import shutil
import os
from io import StringIO
from llama_index.core.memory import ChatMemoryBuffer

memory = ChatMemoryBuffer.from_defaults(token_limit=1500)
#

last = 0
CHROMA_DATA_PATH = "chroma_data/"
EMBED_MODEL =  "sentence-transformers/all-MiniLM-L6-v2" # "BAAI/bge-m3"
#LLM_NAME = "mistralai/Mistral-Nemo-Instruct-2407"
#LLM_NAME = "sswiss-ai/apertus-70b-instruct" # provider: publicai
#LLM_NAME = "openai/gpt-oss-20b"
LLM_NAME = "swiss-ai/apertus-8b-instruct"
#LLM_NAME = "aisingapore/Gemma-SEA-LION-v4-27B-IT"
#LLM_NAME = "W4D/YugoGPT-7B-Instruct-GGUF"
CHUNK_SIZE = 800
CHUNK_OVERLAP = 50
max_results = 3
min_len = 40
min_distance = 0.35
max_distance = 0.6
temperature = 0.7
max_tokens=5100
top_p=0.85
top_k=1000
frequency_penalty=0.0
repetition_penalty=1.12
presence_penalty=0.15
cs = "s0"
sp_flag = True

system_sr = "Zoveš se U-Chat AI asistent i pomažeš odgovorima korisniku usluga kompanije United Group. Korisnik postavlja pitanje ili problem na koji očekuje rešenje. "
# " Ako ne znaš odgovor, reci da ne znaš, ne izmišljaj ga."
system_sr += "Usluge kompanije United Group uključuju i kablovsku mrežu za digitalnu televiziju, pristup internetu, uređaj EON SMART BOX za TV sadržaj, kao i fiksnu telefoniju. "

chroma_client = chromadb.PersistentClient(CHROMA_DATA_PATH)
embedding_func = embedding_functions.SentenceTransformerEmbeddingFunction(
        model_name=EMBED_MODEL
    )

collection = chroma_client.get_or_create_collection(
        name="chroma_data",
        embedding_function=embedding_func,
        metadata={"hnsw:space": "cosine"},
    )
last = collection.count()

#
HF_TOKEN = os.getenv("HF_TOKEN")
#
login(token=(HF_TOKEN))

#system_prompt = system_sr

client = InferenceClient(LLM_NAME)

# "facebook/blenderbot-400M-distill",  facebook/blenderbot-400M-distill, stabilityai/stablelm-zephyr-3b, BAAI/bge-small-en-v1.5
Settings.llm = HuggingFaceInferenceAPI(model_name=LLM_NAME,
#                              device_map="auto",
#                              system_prompt = system_prompt,
                              context_window=4096,
                              max_new_tokens=3072,
#                              stopping_ids=[50278, 50279, 50277, 1, 0],
                              generate_kwargs={"temperature": temperature, "top_p":top_p, "repetition_penalty": repetition_penalty,
                                               "presence_penalty": presence_penalty, "frequency_penalty": frequency_penalty,
                                               "top_k": top_k, "do_sample": False },
#                              tokenizer_kwargs={"max_length": 4096},
                              tokenizer_name=LLM_NAME,
                              hf_token = HF_TOKEN,
                              src = "models",
                              provider="publicai",
                             )
# "BAAI/bge-m3"
Settings.embed_model = HuggingFaceEmbedding(model_name=EMBED_MODEL)
#documents = [Document(text="Content ..."),
#            ]
#index = VectorStoreIndex.from_documents(
#    documents,
#)
vector_store = ChromaVectorStore(chroma_collection=collection)
index = VectorStoreIndex.from_vector_store(vector_store, embed_model=Settings.embed_model)
chat_engine = index.as_chat_engine(chat_mode="condense_plus_context", memory=memory, verbose=True)
# best condense_question context condense_plus_context
#query_engine = index.as_query_engine(verbose=True)

def upload_file(filepath):
    documents = SimpleDirectoryReader(filepath).load_data()
    index = VectorStoreIndex.from_documents(documents)
    #query_engine = index.as_query_engine()
    #condense_question condense_plus_context
    chat_engine = index.as_chat_engine(verbose=True)
    return filepath

def resetChat():
    chat_engine.reset()
    print("Restarted!!!")
    return True

def rag(input_text, history, jezik): # , file):
    global sp_flag
#    if (btn):
#        resetChat()
#    print(history, input_text)
##    if (file):
    documents = []
#!!!    for f in file:
#!!!        documents += SimpleDirectoryReader(f).load_data()
#        f = file + "*.pdf"
##        pathname = os.path.dirname
#        shutil.copyfile(file.name, path)
##        print("pathname=", pathname)
##        print("basename=", os.path.basename(file))
##        print("filename=", file.name)        
##        documents = SimpleDirectoryReader(file).load_data()
#!!!        index2 = VectorStoreIndex.from_documents(documents)
##        query_engine = index2.as_query_engine()
#        return query_engine.query(input_text)
#        return history.append({"role": "assistant", "content": query_engine.query(input_text)})
##        return history + [[input_text, query_engine.query(input_text)]]
#        collection.add(
#            documents=documents,
#        ids=[f"id{last+i}" for i in range(len(documents))],
#        metadatas=[{"state": "s0", "next": "s0", "used": False, "source": 'None', "page": -1, "lang": jezik } for i in range(len(documents)) ]
#    )
##    else:
###    query_results = collection.query(
    #query_engine = index.as_query_engine(
    #    similarity_top_k=3,
    #    vector_store_query_mode="default",
    #    filters=MetadataFilters(
    #         filters=[
    #             ExactMatchFilter(key="lang", value=jezik),
    #        ]
    #    ),
    #    alpha=None,
    #    doc_ids=None,
    #)
    #query_results = index.query(
    #    query_texts = [ input_text ],
    #    n_results = max_results,
    #    where = { "lang": jezik },
    #    #where = { "$and": [ {"lang": jezik}, {"page": { "$nin": [ -1 ]}}]},
    #    #where = { "$and": [ {"$and": [ { "$or": [ {"state": self.cs },  { "page": { "$nin": [ -1 ] } } ] } , { "used": False } ] } ,
    #    #        {"lang": jezik } ] },
    #)
    #jezik = "N/A"
    system_prompt = ""
    match jezik:
        case 'hrvatski':
            o_jezik = 'na hrvatskom jeziku, gramatički točno.'
            system_prompt = system_sr + "Call centar telefon je 095 1000 444 za privatne i 095 1000 500 za poslovne korisnike. Stranica podrške je <https://tele mach.hr/podrska>." + "Odgovaraj isključivo " + o_jezik
        case 'slovenski':
            o_jezik = 'v slovenščini, slovnično pravilen.'
            system_prompt = system_sr + "Call centar i pomoč za fizične uporabnike: 070 700 700.stran za podporo je <https://telemach.si/pomoc>. " + "Odgovor isključivo " + o_jezik
        case 'srpski':
            o_jezik = 'na srpskom jeziku, gramatički ispravno.'
            system_prompt = system_sr + "Call centar telefon je 19900 za sve korisnike. Stranica podrške je <https://sbb.rs/podrska/>. " + "Odgovaraj isključivo " + o_jezik
        case 'makedonski':
            o_jezik = 'на македонски јазикот граматички точно.'
            system_prompt = system_sr + "Stranica podrške je https://mn.nettvplus.com/me/podrska/ za NetTV. " + "Oдговори исклучиво " + o_jezik
        case 'Eksperimentalna opcija':
            o_jezik = 'N/A'
            system_prompt = system_sr + "Call centar telefon je 12755 za Crnu Goru, 0800 31111 za BIH, 070 700 700 u Sloveniji, 19900 u Srbiji, 095 1000 444 za hrvatske korisnike. Odgovori na jeziku istom kao i u postavljenom pitanju ili problemu korisnika."
    print("jezik: "+o_jezik)
    system_prompt = system_prompt + " Sledi pitanje ili problem korisnika, sa kojim dalje komuniciraš: "
    if sp_flag:
        sp_flag = False
    else:
        system_prompt = ""
#        if (o_jezik!='N/A'):
#            input_text += " - odgovori " + o_jezik + "."
#    Settings.llm.system_prompt = system_prompt
    
    response = chat_engine.chat(str({"role": "user", "content": system_prompt + input_text})).response
#    response = query_engine.query(input_text)
    return response
# gr.Textbox(label="Pitanje:", lines=6),
# outputs=[gr.Textbox(label="Odgovor:", lines=6)], 
# ChatMessage(role="assistant", content="Kako Vam mogu pomoći?")
with gr.Blocks() as iface:
    ichat = gr.ChatInterface(fn=rag,        
        title="UChat",
        description="Postavite pitanje ili opišite problem koji imate - nakon promene jezika ili pre početka nove sesije sa agentom pritisnite dugme 'Briši sve - razgovor ispočetka'",
        chatbot=gr.Chatbot(placeholder="Kako Vam mogu pomoći?", type="tuples", label="Agent podrške", height=350),
        textbox=gr.Textbox(placeholder="Pitanje ili opis problema", container=False, scale=7),
        autofocus = True,
        theme="soft",
        examples = [
                    ["Ne radi mi internet", "srpski", ],
                    ["Možete li mi popraviti kompjuter koji koristi internet?", "srpski", ],
                    ["Ne radi mi daljinski upravljač, šta da radim?", "srpski", ],
                    ["EON daljinski upravljalnik mi ne deluje, kaj naj naredim?", "slovenski", ],
                    ["Мојот кабелски прием не работи, што треба да направам?", "makedonski", ],
        ],
        cache_examples=False,
        retry_btn=None,
        undo_btn=None,
        clear_btn="Briši sve - razgovor ispočetka",
        additional_inputs = [gr.Dropdown(["slovenski", "hrvatski", "srpski", "makedonski", "Eksperimentalna opcija"], value="srpski", label="Jezik", info="komunikacije"),
#                             gr.File()
                         ],
        additional_inputs_accordion="Jezik i ostale opcije",
    )
#    login_button = gr.LoginButton("Hugging Face login", size="lg")
    ichat.clear_btn.click(resetChat)

#with gr.Blocks() as iface:
#    gr.Markdown("Uchat")
#    file_out = gr.File()
#    with gr.Row():
#        with gr.Column(scale=1):
#            inp = gr.Textbox(label="Pitanje:", lines=6)
#            u = gr.UploadButton("Upload a file", file_count="single")
#        with gr.Column(scale=1):
#            out = gr.Textbox(label="Odgovor:", lines=6)
#            sub = gr.Button("Pokreni")
#
#    u.upload(upload_file, u, file_out)
#    sub.click(rag, inp, out)
    
iface.launch()