files

app.py

@@ -30,52 +30,6 @@ txt = gr.Textbox(
     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, '
                                                  'using the power of LLMs.')