Update app.py

app.py

@@ -1,62 +1,43 @@
+import logging
+import sys
 import gradio as gr
-from g4f import Provider, models
-from langchain.llms.base import LLM
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 import asyncio
 import nest_asyncio
-from langchain.callbacks.manager import CallbackManager
-from langchain.llms import LlamaCpp
-from llama_index import ServiceContext, LLMPredictor, PromptHelper
-from llama_index.text_splitter import TokenTextSplitter
-from llama_index.node_parser import SimpleNodeParser
-from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings
-from llama_index import SimpleDirectoryReader, VectorStoreIndex
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
+from llama_index.llms import HuggingFaceLLM
+from langchain.embeddings import HuggingFaceEmbeddings
 from g4f import Provider, models
 from langchain.llms.base import LLM
-from llama_index.llms import LangChainLLM 
-from gradio import Interface
+from llama_index.llms import LangChainLLM
+from langchain_g4f import G4FLLM
+
 nest_asyncio.apply()
-from huggingface_hub import hf_hub_download
 
-model_name_or_path = "hlhr202/llama-7B-ggml-int4"
-model_basename = "ggml-model-q4_0.bin" # the model is in bin format
+documents = SimpleDirectoryReader('data').load_data()
 
-#model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename)
+embed_model = HuggingFaceEmbeddings(
+    model_name="sentence-transformers/all-mpnet-base-v2"
+)
 
-n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool.
-n_batch = 256
+async def main(query):
+    llm: LLM = G4FLLM(
+        model=models.gpt_35_turbo,
+        provider=Provider.DeepAi,
+    )
 
+    llm = LangChainLLM(llm=llm)
 
-embed_model = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",
-                                                      model_kwargs={"device": "cpu"})
-"""
-node_parser = SimpleNodeParser.from_defaults(text_splitter=TokenTextSplitter(chunk_size=1024, chunk_overlap=20))
-prompt_helper = PromptHelper(
-  context_window=4096,
-  num_output=256,
-  chunk_overlap_ratio=0.1,
-  chunk_size_limit=None
-)
-"""
-from langchain_g4f import G4FLLM
-llm = LLM = G4FLLM(
-        model=models.gpt_35_turbo,
-        provider=Provider.Acytoo,)
-    
-
-llm = LangChainLLM(llm=llm)
-    
-service_context = ServiceContext.from_defaults(llm=llm,
-  embed_model=embed_model)
-
-documents = SimpleDirectoryReader("data").load_data()
-index = VectorStoreIndex.from_documents(documents, service_context=service_context)
-  
-async def main(question):
-    query_engine = index.as_query_engine(service_context=service_context)
-    response = query_engine.query(question)
-    print(response)
+    service_context = ServiceContext.from_defaults(chunk_size=512, llm=llm, embed_model=embed_model)
+
+    index = VectorStoreIndex.from_documents(documents, service_context=service_context)
+
+    query_engine = index.as_query_engine()
+    response = query_engine.query(query)
     return response
 
-iface = Interface(fn=main, inputs="text", outputs="text")
-iface.launch()
+iface = gr.Interface(fn=main, inputs="text", outputs="text")
+iface.launch()