Update app.py

app.py

@@ -1,168 +1,2 @@
-import gradio as gr
-import spaces
 import os
-from pathlib import Path
-from llama_index.core.tools import QueryEngineTool
-from llama_index.core import VectorStoreIndex
-from llama_index.core import Settings
-from llama_index.core import SimpleDirectoryReader
-from llama_index.llms.groq import Groq
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from typing import Tuple
-from llama_index.core import StorageContext, load_index_from_storage
-from llama_index.core.objects import ObjectIndex
-from llama_index.core.agent import ReActAgent
-import torch
-import sys
-import io
-
-@spaces.GPU()
-def create_doc_tools(document_fp: str, doc_name: str, verbose: bool = True) -> Tuple[QueryEngineTool,]:
-    documents = SimpleDirectoryReader(input_files=[document_fp]).load_data()
-
-    llm = Groq(model="mixtral-8x7b-32768")
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    #local_model_path = "/home/user/app/sentence-transformers--all-mpnet-base-v2"
-    #embed_model = HuggingFaceEmbedding(model_name=local_model_path, device=device)
-    embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-mpnet-base-v2", device=device)
-    Settings.llm = llm
-    Settings.embed_model = embed_model
-
-    load_dir_path = f"/home/user/app/agentic_index_st/{doc_name}"
-    storage_context = StorageContext.from_defaults(persist_dir=load_dir_path)
-    vector_index = load_index_from_storage(storage_context)
-    vector_query_engine = vector_index.as_query_engine()
-
-    vector_tool = QueryEngineTool.from_defaults(
-        name=f"{doc_name}_vector_query_engine_tool",
-        query_engine=vector_query_engine,
-        description=f"Useful for retrieving specific context from the {doc_name}.",
-    )
-
-    return vector_tool
-
-def find_tex_files(directory: str):
-    tex_files = []
-    for root, dirs, files in os.walk(directory):
-        for file in files:
-            if file.endswith(('.tex', '.txt')):
-                file_path = os.path.abspath(os.path.join(root, file))
-                tex_files.append(file_path)
-    tex_files.sort()
-    return tex_files
-
-def initialize_agent(apikey):
-    os.environ["GROQ_API_KEY"] = apikey
-
-    llm = Groq(model="mixtral-8x7b-32768")
-
-    try:
-        directory = '/home/user/app/rag_docs_final_review_tex_merged'
-        tex_files = find_tex_files(directory)
-        
-        paper_to_tools_dict = {}
-        for paper in tex_files:
-            path = Path(paper)
-            vector_tool = create_doc_tools(doc_name=path.stem, document_fp=path)
-            paper_to_tools_dict[path.stem] = [vector_tool]
-    
-        initial_tools = [t for paper in tex_files for t in paper_to_tools_dict[Path(paper).stem]]
-    
-        obj_index = ObjectIndex.from_objects(
-            initial_tools,
-            index_cls=VectorStoreIndex,
-        )
-    
-        obj_retriever = obj_index.as_retriever(similarity_top_k=6)
-    
-        context = """You are an agent designed to answer scientific queries over a set of given documents.
-        Please always use the tools provided to answer a question. Do not rely on prior knowledge.
-        """
-    
-        agent = ReActAgent.from_tools(
-            tool_retriever=obj_retriever,
-            llm=llm,
-            verbose=True,
-            context=context
-        )
-        return agent
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-def chat_with_agent(prompt, agent, verbose_toggle):
-    try:
-        # Check if agent initialization was successful
-        if isinstance(agent, str) and agent.startswith("Error:"):
-            return agent  # Return the error message if initialization failed
-
-        # Redirect stdout
-        original_stdout = sys.stdout
-        sys.stdout = io.StringIO()
-
-        # query the agent
-        response = agent.query(prompt)
-
-        # Get the captured output and restore stdout
-        output = sys.stdout.getvalue()
-        sys.stdout = original_stdout
-
-        # format the received verbose output
-        verbose = ''
-        for output_string in output.split('==='):
-            verbose += output_string
-            verbose += '\n'
-
-        # assistant response
-        msg = f'{verbose}' if verbose_toggle else f'{response.response[10:]}'
-        
-        return msg
-    except Exception as e:
-        return str(e)
-
-def main():
-    agent = None
-
-    def set_apikey(apikey):
-        nonlocal agent
-        agent = initialize_agent(apikey)
-        if isinstance(agent, str) and agent.startswith("Error:"):
-            return agent  # Return the error message if initialization failed
-        return "API Key Set. You may start asking questions now."
-
-    def reset_chat():
-        nonlocal agent
-        agent = None
-        return "Chat reset. You may start asking questions now."
-
-    def chat_function(prompt, apikey, verbose_toggle):
-        nonlocal agent
-        if not agent:
-            api_response = set_apikey(apikey)
-            if isinstance(agent, str) and agent.startswith("Error:"):
-                return api_response  # Return the error message if initialization failed
-        return chat_with_agent(prompt, agent, verbose_toggle)
-
-    with gr.Blocks() as demo:
-        gr.Markdown("# AMGPT, Powered by LlamaIndex")
-
-        with gr.Row():
-            apikey = gr.Textbox(label="Enter your Groq API Key", type="password")
-            set_apikey_button = gr.Button("Set API Key")
-        
-        set_apikey_button.click(set_apikey, inputs=apikey, outputs=None)
-
-        with gr.Row():
-            verbose_toggle = gr.Checkbox(label="Verbose", value=True)
-            reset = gr.Button("Reset Chat")
-        
-        reset.click(reset_chat, outputs=None)
-
-        prompt = gr.Textbox(label="Ask a question")
-        output = gr.Textbox(label="Response")
-
-        prompt.submit(chat_function, inputs=[prompt, apikey, verbose_toggle], outputs=output)
-
-    demo.launch()
-
-if __name__ == "__main__":
-    main()
+print(os.getcwd())