New streamlit committed

app.py

@@ -1,4 +1,64 @@
 import streamlit as st
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain_community.llms import LlamaCpp
+from llama_cpp import Llama
+from pinecone import Pinecone
+from huggingface_hub import hf_hub_download
+@st.cache_resource()
+def load_model():
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+    # from google.colab import userdata
+    model_name_or_path = "CompendiumLabs/bge-large-en-v1.5-gguf"
+    model_basename = 'bge-large-en-v1.5-f32.gguf'
+    model_path = hf_hub_download(
+    repo_id=model_name_or_path,
+    filename=model_basename,
+    cache_dir= '/content/models' # Directory for the model
+)
+    model = Llama(model_path, embedding=True)
+
+    st.success("Loaded NLP model from Hugging Face!")  # 👈 Show a success message
+    
+    
+    # pc = Pinecone(api_key=api_key)
+    # index = pc.Index("law")
+#     model_2_name = "TheBloke/zephyr-7B-beta-GGUF"
+#     model_2base_name = "zephyr-7b-beta.Q4_K_M.gguf"
+#     model_path_model = hf_hub_download(
+#     repo_id=model_2_name,
+#     filename=model_2base_name,
+#     cache_dir= '/content/models' # Directory for the model
+# )
+#     prompt_template = "<|system|>\
+# </s>\
+# <|user|>\
+# {prompt}</s>\
+# <|assistant|>"
+#     template = prompt_template
+#     prompt = PromptTemplate.from_template(template)
+#     callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+#     llm = LlamaCpp(
+#     model_path=model_path_model,
+#     temperature=0.75,
+#     max_tokens=2500,
+#     top_p=1,
+#     callback_manager=callback_manager,
+#     verbose=True, 
+#     n_ctx=2048,
+#     n_threads = 2# Verbose is required to pass to the callback manager
+# )
+    return model
+    
+st.title("Please ask your question on Lithuanian rules for foreigners.")
+a  = load_model()
+question = st.text_input("Enter your question:")
+# if question:
+#     # Perform Question Answering
+#     answer = qa_chain(context=context, question=question)
+    
+#     # Display the answer
+#     st.header("Answer:")
+#     st.write(answer)

requirements.txt

@@ -0,0 +1,4 @@
+huggingface-hub
+pinecone-client
+llama-cpp-python -C cmake.args="-DLLAMA_BLAS=ON;-DLLAMA_BLAS_VENDOR=OpenBLAS"
+langchain