Update app.py

app.py

@@ -1,76 +1,52 @@
 import logging
 import sys
-
-logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
-
-import git
-from llama_index import SimpleDirectoryReader
-
+import streamlit as st
 from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
-documents = SimpleDirectoryReader("./").load_data()
-
-
-
-import torch
-
 from llama_index.llms import LlamaCPP
 from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt
-llm = LlamaCPP(
-    # You can pass in the URL to a GGML model to download it automatically
-    model_url='https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf',
-    # optionally, you can set the path to a pre-downloaded model instead of model_url
-    model_path=None,
-    temperature=0.1,
-    max_new_tokens=256,
-    # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
-    context_window=3900,
-    # kwargs to pass to __call__()
-    generate_kwargs={},
-    # kwargs to pass to __init__()
-    # set to at least 1 to use GPU
-    model_kwargs={"n_gpu_layers": -1},
-    # transform inputs into Llama2 format
-    messages_to_prompt=messages_to_prompt,
-    completion_to_prompt=completion_to_prompt,
-    verbose=True,
-)
-
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
 from llama_index.embeddings import LangchainEmbedding
 
-embed_model = LangchainEmbedding(
-  HuggingFaceEmbeddings(model_name="thenlper/gte-large")
-)
-
-service_context = ServiceContext.from_defaults(
-    chunk_size=256,
-    llm=llm,
-    embed_model=embed_model
-)
-
-index = VectorStoreIndex.from_documents(documents, service_context=service_context)
-
-
-# Create a Streamlit app file (e.g., app.py) and run it
-
-import streamlit as st
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-
-def generate_response(prompt):
-    model_name = "gpt2"
-    model = GPT2LMHeadModel.from_pretrained(model_name)
-    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-
-    input_ids = tokenizer.encode(prompt, return_tensors="pt")
-    output = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2)
+# Set up logging
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
 
-    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
-    return generated_text
+def configure_llama_model():
+    model_url = 'https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf'
+    llm = LlamaCPP(
+        model_url=model_url,
+        temperature=0.1,
+        max_new_tokens=256,
+        context_window=3900,
+        model_kwargs={"n_gpu_layers": -1},
+        messages_to_prompt=messages_to_prompt,
+        completion_to_prompt=completion_to_prompt,
+        verbose=True,
+    )
+    return llm
+
+def configure_embeddings():
+    embed_model = LangchainEmbedding(
+        HuggingFaceEmbeddings(model_name="thenlper/gte-large")
+    )
+    return embed_model
+
+def configure_service_context(llm, embed_model):
+    return ServiceContext.from_defaults(chunk_size=256, llm=llm, embed_model=embed_model)
+
+def initialize_vector_store_index(data_path, service_context):
+    documents = SimpleDirectoryReader(data_path).load_data()
+    return VectorStoreIndex.from_documents(documents, service_context=service_context)
 
 def main():
     st.title("Cloudflare RAG")
 
+    # Configure and initialize components
+    llm = configure_llama_model()
+    embed_model = configure_embeddings()
+    service_context = configure_service_context(llm, embed_model)
+    index = initialize_vector_store_index("/content/Data/", service_context)
+
     # User input
     user_input = st.text_input("Enter your message:")
 
@@ -83,4 +59,4 @@ def main():
         st.text_area("ChatGPT Response:", response, height=100)
 
 if __name__ == "__main__":
-    main()
+    main()