files

app.py

@@ -1,13 +1,11 @@
-import os
-os.system("pip uninstall -y gradio") 
-os.system("pip install gradio==3.31.0") 
-from sentence_transformers import SentenceTransformer, models
+#!/usr/bin/env python3
 from dotenv import load_dotenv
 from langchain.chains import RetrievalQA
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain.vectorstores import Chroma
 from langchain.llms import GPT4All, LlamaCpp
+import os
 import argparse
 from pathlib import Path
 import base64
@@ -24,11 +22,51 @@ model_n_ctx = os.environ.get('MODEL_N_CTX')
 
 from constants import CHROMA_SETTINGS
 
-# Declare Textbox globally
-txt = gr.Textbox(
-    label="Type your query here:",
-    placeholder="What would you like to learn today?"
-).style(container=True)
+def main():
+    # Parse the command line arguments
+    args = parse_arguments()
+    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
+    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
+    retriever = db.as_retriever()
+    # activate/deactivate the streaming StdOut callback for LLMs
+    callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]
+    # Prepare the LLM
+    '''match model_type:
+        case "LlamaCpp":
+            llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, callbacks=callbacks, verbose=False)
+        case "GPT4All":
+            llm = GPT4All(model=model_path, n_ctx=model_n_ctx, backend='gptj', callbacks=callbacks, verbose=False)
+        case _default:
+            print(f"Model {model_type} not supported!")
+            exit;'''
+    if model_type == "LlamaCpp":
+        llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, callbacks=callbacks, verbose=False)
+    elif model_type == "GPT4All":
+        llm = GPT4All(model=model_path, n_ctx=model_n_ctx, backend='gptj', callbacks=callbacks, verbose=False)
+    else:
+        print(f"Model {model_type} not supported!")
+        exit;
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents= not args.hide_source)
+    # Interactive questions and answers
+    while True:
+        query = input("\nEnter a query: ")
+        if query == "exit":
+            break
+
+        # Get the answer from the chain
+        res = qa(query)
+        answer, docs = res['result'], [] if args.hide_source else res['source_documents']
+
+        # Print the result
+        print("\n\n> Question:")
+        print(query)
+        print("\n> Answer:")
+        print(answer)
+
+        # Print the relevant sources used for the answer
+        for document in docs:
+            print("\n> " + document.metadata["source"] + ":")
+            print(document.page_content)
 
 def parse_arguments():
     parser = argparse.ArgumentParser(description='privateGPT: Ask questions to your documents without an internet connection, '
@@ -224,5 +262,5 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css, title="RyBOT") as demo:
         inputs=None, 
         outputs=[txt]
     )
-
+    
 demo.launch()