app.py

import gradio as gr
import os
import logging
from llama_index.llms.gemini import Gemini
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

GOOGLE_API_KEY = "AIzaSyDYhyRoOWBJWOb4bqY5wmFLrBo4HTwQDko"  # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
 
from llama_index.core import SimpleDirectoryReader
from g4f import Provider, models
from langchain.llms.base import LLM

from llama_index.llms.langchain import LangChainLLM
from langchain_g4f import G4FLLM

from llama_index.core import (
    ServiceContext,
    SimpleDirectoryReader,
    StorageContext,
    VectorStoreIndex,
    set_global_service_context,
)
#from llama_index.llms import Gemini
from llama_index.embeddings.gemini import GeminiEmbedding
import g4f 
g4f.debug.logging = True 
from llama_index.core import Settings
from langchain_google_genai import ChatGoogleGenerativeAI


llm= LLM = G4FLLM(
        model=models.gpt_35_turbo_16k,
    )

llm = LangChainLLM(llm=llm)



safe = [
    {
        "category": "HARM_CATEGORY_HARASSMENT",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_HATE_SPEECH",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
        "threshold": "BLOCK_NONE",
    },
]



#llm = Gemini(model="models/gemini-pro", safety_settings=safe)

model_name = "models/embedding-001"


#llm = Gemini()
embed_model = GeminiEmbedding(
    model_name=model_name, api_key=GOOGLE_API_KEY, title="this is a document"
)
Settings.embed_model = embed_model
# Reads pdfs at "./" path


"""
parser = LlamaParse(
    api_key="llx-KMCDGpt3Yn89wwOYJXaFDfJLHTbUQbnTKVccaGVHJLfAN96w",  # can also be set in your env as LLAMA_CLOUD_API_KEY
    result_type="markdown",  # "markdown" and "text" are available
    verbose=True
)

file_extractor = {".pdf": parser}
documents = SimpleDirectoryReader("./data", file_extractor=file_extractor).load_data()

"""



documents = (
    SimpleDirectoryReader(
        input_dir = 'data',
        required_exts = [".pdf"])
        .load_data()
)

# ServiceContext is a bundle of commonly used 
# resources used during the indexing and 
# querying stage 
service_context = (
    ServiceContext
    .from_defaults(
        llm=llm, 
        embed_model=embed_model, 
        chunk_size=8045
    )
)
set_global_service_context(service_context)
print("node passer11")
# Node represents a “chunk” of a source Document
nodes = (
    service_context
    .node_parser
    .get_nodes_from_documents(documents)
)
print("node passer")
# offers core abstractions around storage of Nodes, 
# indices, and vectors
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
print("node passer")
# Create the vectorstore index
index = (
    VectorStoreIndex
    .from_documents(
        documents, 
        storage_context=storage_context, 
        llm=llm
        )
)
print("node passer")

query_engine = index.as_query_engine()
# Query the index


def greet(name):
    ss = name + ".réponds en citant tes sources et articles"
    response = query_engine.query(ss)

    print("question :",name)
    print("réponse :", response)
    return response

iface = gr.Interface(fn=greet, inputs=gr.Textbox(label="Question:", lines=4), outputs="text")
iface.launch()