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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+1.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,2 @@
----
-title: Nexaagent
-emoji: 💻
-colorFrom: pink
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.27.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# autogen_langchain_private
+ 

requirements.txt

@@ -0,0 +1,10 @@
+requests
+beautifulsoup4
+streamlit
+pyautogen~=0.1.0
+docker 
+langchain 
+openai
+tiktoken
+chromadb 
+pypdf

streamlit_test_catch.py

@@ -0,0 +1,198 @@
+import streamlit as st
+import os
+import autogen
+import base64
+from langchain.vectorstores import Chroma
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import PyPDFLoader
+from langchain.memory import ConversationBufferMemory
+from langchain.llms import OpenAI
+from langchain.chains import ConversationalRetrievalChain
+import io
+import sys
+import tempfile
+import openai
+import multiprocessing
+import autogen.agentchat.user_proxy_agent as upa
+
+class OutputCapture:
+    def __init__(self):
+        self.contents = []
+
+    def write(self, data):
+        self.contents.append(data)
+
+    def flush(self):
+        pass
+
+    def get_output_as_string(self):
+        return ''.join(self.contents)
+
+class ExtendedUserProxyAgent(upa.UserProxyAgent):
+    def __init__(self, *args, log_file="interaction_log.txt", **kwargs):
+        super().__init__(*args, **kwargs)
+        self.log_file = log_file
+
+    def log_interaction(self, message):
+        with open(self.log_file, "a") as f:
+            f.write(message + "\n")
+
+    def get_human_input(self, *args, **kwargs):
+        human_input = super().get_human_input(*args, **kwargs)
+        self.log_interaction(f"Human input: {human_input}")
+        return human_input
+    
+# Example usage:
+config_list = [
+    {
+        "model": "gpt-4",
+        "api_key": "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",
+    }
+]
+
+gpt4_api_key = config_list[0]["api_key"]
+os.environ['OPENAI_API_KEY'] = gpt4_api_key
+openai.api_key = os.environ["OPENAI_API_KEY"]
+
+def build_vector_store(pdf_path, chunk_size=1000):
+    loaders = [PyPDFLoader(pdf_path)]
+    docs = []
+    for l in loaders:
+        docs.extend(l.load())
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size)
+    docs = text_splitter.split_documents(docs)
+    vectorstore = Chroma(
+        collection_name="full_documents",
+        embedding_function=OpenAIEmbeddings()
+    )
+    vectorstore.add_documents(docs)
+    return vectorstore
+
+def setup_qa_chain(vectorstore):
+    qa = ConversationalRetrievalChain.from_llm(
+        OpenAI(temperature=0),
+        vectorstore.as_retriever(),
+        memory=ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    )
+    return qa
+
+def get_image_as_base64_string(path):
+    with open(path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode()
+    
+def answer_uniswap_question(question, qa_chain):
+    response = qa_chain({"question": question})
+    return response["answer"]
+
+def setup_agents(config_list, answer_function):
+    llm_config = {
+        "request_timeout": 600,
+        "seed": 42,
+        "config_list": config_list,
+        "temperature": 0,
+        "functions": [
+            {
+                "name": "answer_uniswap_question",
+                "description": "Answer any Uniswap related questions",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "question": {
+                            "type": "string",
+                            "description": "The question to ask in relation to Uniswap protocol",
+                        }
+                    },
+                    "required": ["question"],
+                },
+            }
+        ],
+    }
+    assistant = autogen.AssistantAgent(name="assistant", llm_config=llm_config)
+    user_proxy = ExtendedUserProxyAgent(
+        name="user_proxy",
+        human_input_mode="NEVER",
+        max_consecutive_auto_reply=10,
+        code_execution_config={"work_dir": "."},
+        llm_config=llm_config,
+        system_message="""Reply TERMINATE if the task has been solved at full satisfaction.
+Otherwise, reply CONTINUE, or the reason why the task is not solved yet.""",
+        function_map={"answer_uniswap_question": answer_function}
+    )
+    return assistant, user_proxy
+
+def initiate_task(user_proxy, assistant, user_question):
+    user_proxy.initiate_chat(
+        assistant,
+        message= user_question
+            )
+    
+def initiate_task_process(queue, tmp_path, user_question):
+    loaders = [PyPDFLoader(tmp_path)]
+    vectorstore = build_vector_store(tmp_path)
+    qa_chain = setup_qa_chain(vectorstore)
+    assistant, user_proxy = setup_agents(config_list, lambda q: answer_uniswap_question(q, qa_chain))
+
+    output_capture = OutputCapture()
+    sys.stdout = output_capture
+    initiate_task(user_proxy, assistant, user_question)
+    queue.put(output_capture.get_output_as_string())
+
+def app():
+    st.title("NexaAgent 0.0.1")
+    
+    # Sidebar introduction
+    st.sidebar.header("About NexaAgent 0.0.1")
+    st.sidebar.markdown("""
+        🚀 **Introducing NexaAgent 0.0.1!** 
+        A highly efficient PDF tool for all your needs.
+        
+        📄 Upload any PDF, no matter its size or the task type.
+        
+        ✅ Guaranteed accuracy, significantly reducing any discrepancies.
+        
+        🔧 Empowered by:
+        - **AutoGen** 🛠️
+        - **LangChain** 🌐
+        - **chromadb** 🗄️
+    """)
+    image_path = "1.png"
+    st.sidebar.image(image_path, caption="Your Caption Here", use_column_width=True)
+
+
+    # Create left and right columns
+    col1, col2 = st.columns(2)
+
+    with col1:
+        # Upload PDF file
+        uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf"])
+
+        if uploaded_file:
+            with st.spinner("Processing PDF..."):
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+                    tmp.write(uploaded_file.getvalue())
+                    tmp_path = tmp.name
+
+            # User input for question
+            user_question = st.text_area("Enter your task:", height=300)
+            if user_question:
+                with st.spinner("Fetching the answer..."):
+                    # 使用进程来执行可能引发错误的代码
+                    queue = multiprocessing.Queue()
+                    process = multiprocessing.Process(target=initiate_task_process, args=(queue, tmp_path, user_question))
+                    process.start()
+                    process.join()
+
+                    # 从队列中获取结果
+                    captured_output = queue.get()
+                    col2.text_area("", value=captured_output, height=600)
+
+if __name__ == "__main__":
+    st.set_page_config(layout="wide")
+    app()
+
+
+
+
+
+