Upload app (3).py

app (3).py

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+#!/usr/bin/env python
+import os
+import shutil
+import json
+import torch
+import transformers
+import chardet
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.models.llama.configuration_llama import LlamaConfig
+from huggingface_hub import hf_hub_download
+import gradio as gr
+
+# --- 解決 Matplotlib 權限問題 ---
+os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"
+
+# === Enterprise Environment Settings ===
+os.environ["HOME"] = "/tmp"
+os.environ["XDG_CACHE_HOME"] = "/tmp/.cache"
+os.environ["HF_HOME"] = "/tmp/huggingface"
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface/transformers"
+os.environ["HF_DATASETS_CACHE"] = "/tmp/huggingface/datasets"
+os.environ["HF_METRICS_CACHE"] = "/tmp/huggingface/metrics"
+os.environ["GRADIO_FLAGGING_DIR"] = "/tmp/flagged"
+os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp/sentence_transformers"
+os.environ["HF_HUB_CACHE"] = "/tmp/huggingface/hf_cache"
+os.environ["HF_HUB_DOWNLOAD_TIMEOUT"] = "60"
+
+# === Load Required Modules ===
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma, FAISS
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain.llms import HuggingFacePipeline
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.document_loaders import PyPDFLoader, TextLoader, UnstructuredWordDocumentLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.summarize import load_summarize_chain
+from tempfile import mkdtemp
+import pandas as pd
+
+# === Multi-Agent Imports ===
+from serpapi import GoogleSearch
+# CrewAI 部分：完全使用 CrewAI 的 Agent、Task、Crew 與 @tool 裝飾器
+from crewai import Crew, Agent, Task, Process
+from crewai.tools import tool
+from langchain_experimental.agents import create_pandas_dataframe_agent
+
+session_retriever = None
+session_qa_chain = None
+csv_dataframe = None  # CSV tool will use this
+# === Model and Device Setup ===
+if torch.backends.mps.is_available():
+    device = "mps"
+elif torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+print(f"Using device => {device}")
+
+hf_token = os.environ.get("HF_TOKEN")
+openai_api_key = os.environ.get("OPENAI_API_KEY")
+model_id = "ChienChung/my-llama-1b"
+
+config_path = hf_hub_download(
+    repo_id=model_id,
+    filename="config.json",
+    use_auth_token=hf_token,
+    cache_dir="/tmp/huggingface"
+)
+with open(config_path, "r", encoding="utf-8") as f:
+    config_dict = json.load(f)
+if "rope_scaling" in config_dict:
+    config_dict["rope_scaling"] = {"type": "dynamic", "factor": config_dict["rope_scaling"].get("factor", 32.0)}
+model_config = LlamaConfig.from_dict(config_dict)
+model_config.trust_remote_code = True
+
+print("Loading Llama model...")
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    config=model_config,
+    trust_remote_code=True,
+    use_auth_token=hf_token,
+    cache_dir="/tmp/huggingface"
+)
+model.to(device)
+print("Model loaded!")
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    use_auth_token=hf_token,
+    cache_dir="/tmp/huggingface"
+)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+print("Tokenizer loaded!")
+
+query_pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    device_map="auto" if device != "cpu" else None,
+    do_sample=False,
+    temperature=0.0,
+    max_new_tokens=200,
+    return_full_text=False
+)
+
+# === Chroma DB and Document Retrieval Setup ===
+print("Loading Chroma DB for Biden Speech...")
+if not os.path.exists("/tmp/chroma_db"):
+    shutil.copytree("./chroma_db", "/tmp/chroma_db")
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+vectordb = Chroma(persist_directory="/tmp/chroma_db", embedding_function=embeddings)
+retriever = vectordb.as_retriever()
+
+custom_prompt = PromptTemplate(
+    input_variables=["context", "question"],
+    template="""You are a helpful AI assistant. Use only the text from the context below to answer the user's question.
+If the answer is not in the context, say "No relevant info found."
+If the question is not in the context, say "No relevant info found."
+
+Return only the final answer in one to three sentences.
+Do not restate the question or context.
+Do not include these instructions in your final output.
+
+Context:
+{context}
+
+Question: {question}
+
+Answer:
+"""
+)
+
+llm_local = HuggingFacePipeline(pipeline=query_pipeline)
+llm_gpt4 = ChatOpenAI(model_name="gpt-4o-mini", temperature=0.2, openai_api_key=openai_api_key)
+crew_llm = ChatOpenAI(
+    model_name="gpt-4o-mini",
+    temperature=0.2,
+    openai_api_key=openai_api_key
+)
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+qa_gpt = ConversationalRetrievalChain.from_llm(
+    llm=llm_gpt4,
+    retriever=retriever,
+    memory=memory,
+    combine_docs_chain_kwargs={"prompt": custom_prompt}
+)
+
+# === Helper Function：從上傳文件中提取檔案路徑 ===
+def get_file_path(file):
+    if isinstance(file, str):
+        return file
+    elif isinstance(file, dict):
+        # 優先使用 "data" 鍵，其次是 "name"
+        return file.get("data", file.get("name", None))
+    elif hasattr(file, "save"):
+        temp_dir = mkdtemp()
+        file_path = os.path.join(temp_dir, file.name)
+        file.save(file_path)
+        return file_path
+    else:
+        return None
+
+# === 原有功能（Tab 1～Tab 4）函式 ===
+def rag_llama_qa(query):
+    output = RetrievalQA.from_chain_type(
+        llm=llm_local,
+        chain_type="stuff",
+        retriever=retriever,
+        return_source_documents=False,
+        chain_type_kwargs={"prompt": custom_prompt}
+    ).run(query)
+    lower_text = output.lower()
+    idx = lower_text.find("answer:")
+    return output[idx + len("answer:"):].strip() if idx != -1 else output
+
+def rag_gpt4_qa(query):
+    return qa_gpt.run(query)
+
+def upload_and_chat(file, query):
+    file_path = get_file_path(file)
+    if file_path is None:
+        return "Unable to obtain the uploaded file path."
+    if file_path.lower().endswith(".pdf"):
+        loader = PyPDFLoader(file_path)
+    elif file_path.lower().endswith(".docx"):
+        loader = UnstructuredWordDocumentLoader(file_path)
+    else:
+        loader = TextLoader(file_path)
+    docs = loader.load()
+    chunks = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50).split_documents(docs)
+    db = FAISS.from_documents(chunks, embeddings)
+    temp_retriever = db.as_retriever()
+    qa_temp = RetrievalQA.from_chain_type(
+        llm=llm_gpt4,
+        chain_type="stuff",
+        retriever=temp_retriever,
+        return_source_documents=False,
+        chain_type_kwargs={"prompt": custom_prompt}
+    )
+    return qa_temp.run(query)
+
+initial_prompt = PromptTemplate(
+    input_variables=["text"],
+    template="""Write a concise and structured summary of the following content. Focus on capturing the main ideas and key details:
+
+{text}
+
+--- Summary ---
+"""
+)
+refine_prompt = PromptTemplate(
+    input_variables=["existing_answer", "text"],
+    template="""You already have an existing summary:
+{existing_answer}
+
+Refine the summary based on the new content below. Add or update information only if it's relevant. Keep it concise:
+
+{text}
+
+--- Refined Summary ---
+"""
+)
+
+def document_summarize(file):
+    file_path = get_file_path(file)
+    if file_path is None:
+        return "Unable to obtain the uploaded file."
+    if file_path.lower().endswith(".pdf"):
+        loader = PyPDFLoader(file_path)
+    elif file_path.lower().endswith(".docx"):
+        loader = UnstructuredWordDocumentLoader(file_path)
+    else:
+        loader = TextLoader(file_path)
+    docs = loader.load()
+    summarize_chain = load_summarize_chain(llm_gpt4, chain_type="refine", question_prompt=initial_prompt, refine_prompt=refine_prompt)
+    summary = summarize_chain.invoke(docs)
+    return summary['output_text']
+
+
+def csv_agent(file, query):
+    file_path = get_file_path(file)
+    if file_path is None:
+        return "Unable to obtain the uploaded CSV file."
+    try:
+        with open(file_path, 'rb') as f:
+            result = chardet.detect(f.read())
+            encoding = result['encoding']
+        df = pd.read_csv(file_path, encoding=encoding)
+    except Exception as e:
+        return f"Error reading CSV: {e}"
+    safe_dict = {"df": df, "pd": pd}
+    try:
+        result = eval(query, {"__builtins__": {}}, safe_dict)
+        return str(result)
+    except Exception as e:
+        return f"Query error: {e}"
+
+def search_agent(query):
+    api_key = os.environ.get("SERPAPI_API_KEY")
+    if not api_key:
+        return "SERPAPI_API_KEY not set. Please set the environment variable."
+    params = {"engine": "google", "q": query, "api_key": api_key, "num": 5}
+    search = GoogleSearch(params)
+    results = search.get_dict()
+    if "organic_results" in results:
+        output = ""
+        for result in results["organic_results"]:
+            title = result.get("title", "No Title")
+            link = result.get("link", "No Link")
+            snippet = result.get("snippet", "No Snippet")
+            output += f"Title: {title}\nLink: {link}\nSnippet: {snippet}\n\n"
+        return output.strip()
+    else:
+        return "No results found."
+
+def uploaded_qa(file, query):
+    file_path = get_file_path(file)
+    if file_path is None:
+        return "Unable to obtain the uploaded file path."
+    if file_path.lower().endswith(".pdf"):
+        loader = PyPDFLoader(file_path)
+    elif file_path.lower().endswith(".docx"):
+        loader = UnstructuredWordDocumentLoader(file_path)
+    else:
+        loader = TextLoader(file_path)
+    docs = loader.load()
+    chunks = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50).split_documents(docs)
+    db = FAISS.from_documents(chunks, embeddings)
+    temp_retriever = db.as_retriever()
+    qa_temp = RetrievalQA.from_chain_type(
+        llm=llm_gpt4,
+        chain_type="stuff",
+        retriever=temp_retriever,
+        return_source_documents=False,
+        chain_type_kwargs={"prompt": custom_prompt}
+    )
+    return qa_temp.run(query)
+
+# === CrewAI Multi-Agent System (Tab 5) ===
+# 完全捨棄 langchain.agents.Tool，使用 CrewAI 的 @tool 裝飾器來定義工具
+from pydantic import BaseModel
+class SimpleQuery(BaseModel):
+    query: str
+
+@tool("summarise")
+def summarise_tool(query: str) -> str:
+    """Summarise: Use document summarisation functionality."""
+    global session_retriever, session_qa_chain
+    if session_retriever is None:
+        return "尚未上傳文件。"
+    try:
+        docs = session_retriever.get_relevant_documents(query if query.strip() else "summary")
+        if not docs:
+            return "無法從文件中找到相關內容。"
+        summarize_chain = load_summarize_chain(llm_gpt4, chain_type="refine", question_prompt=initial_prompt, refine_prompt=refine_prompt)
+        summary = summarize_chain.invoke(docs)
+        return summary['output_text']
+    except Exception as e:
+        return f"摘要錯誤: {e}"
+
+@tool("python_calc")
+def python_calc_tool(query: str) -> str:
+    """Python Calculation: Perform basic arithmetic or logical operations."""
+    try:
+        return str(eval(query))
+    except Exception as e:
+        return f"計算錯誤: {e}"
+
+@tool("search_agent")
+def search_tool_func(query: str) -> str:
+    """Search: Perform web searches using external search engines."""
+    return search_agent(query)
+
+@tool("uploaded_qa")
+def uploaded_qa_tool_func(query: str) -> str:
+    """Document QA: Answer questions based on the uploaded document content."""
+    global session_qa_chain
+    if session_qa_chain is not None:
+        try:
+            return session_qa_chain.run(query)
+        except Exception as e:
+            return f"文檔問答錯誤: {e}"
+    else:
+        return "尚未上傳文件。"
+        
+@tool("csv_agent")
+def csv_tool_func(query: str) -> str:
+    """CSV Agent: Use natural language to analyse uploaded CSV files."""
+    global csv_dataframe
+    if csv_dataframe is None:
+        return "No CSV file uploaded."
+    try:
+        agent = create_pandas_dataframe_agent(llm=llm_gpt4, df=csv_dataframe, verbose=True)
+        return agent.run(f"Here is the table:\n{csv_dataframe.head().to_string(index=False)}\n\n{query}")
+    except Exception as e:
+        return f"CSV Agent error: {e}"
+
+# 建立 CrewAI 代理（僅針對 Tab 5）
+summarizer_agent = Agent(
+    role="Document Summarizer",
+    goal="Summarise the content of the uploaded document.",
+    backstory="You are a professional summarisation expert who can identify key points in long documents.",
+    tools=[summarise_tool],
+    verbose=True
+)
+document_qa_agent = Agent(
+    role="Document QA Specialist",
+    goal="Answer questions based on the uploaded document.",
+    backstory="You are an expert in document understanding and can accurately extract answers.",
+    tools=[uploaded_qa_tool_func],
+    verbose=True
+)
+
+search_agent = Agent(
+    role="Search Expert",
+    goal="Search the web and provide relevant information.",
+    backstory="You are an expert at finding relevant information from the internet.",
+    tools=[search_tool_func],
+    verbose=True
+)
+
+math_agent = Agent(
+    role="Math Assistant",
+    goal="Perform accurate arithmetic or logical calculations.",
+    backstory="You are a calculator expert skilled at quick computations.",
+    tools=[python_calc_tool],
+    verbose=True
+)
+csv_agent = Agent(
+    role="CSV Analyst",
+    goal="Analyse tabular data and answer questions about the uploaded CSV file.",
+    backstory="You are skilled in interpreting tabular datasets and can extract numerical or logical insights.",
+    tools=[csv_tool_func],
+    verbose=True
+)
+router_agent = Agent(
+    role="Query Router",
+    goal="Determine the most suitable agent or tool to handle the user query.",
+    backstory="You are an intelligent query dispatcher that analyses the user's intent and chooses the best AI agent to answer.",
+    tools=[python_calc_tool, search_tool_func, csv_tool_func, uploaded_qa_tool_func, summarise_tool],
+    verbose=True
+)
+router_task = Task(
+    description=(
+        "Based on the user's query, decide which agent or tool is best suited to handle it:\n"
+        "- If the query is related to the content of an uploaded file (e.g., 'what is this document about?'), send it to the **Document QA Agent**.\n"
+        "- If the query contains words like 'summarize', 'summary', or 'main points', use the **Summarizer Agent**.\n"
+        "- If the query involves numbers, calculations, or logic (e.g., '50 * 23 - 5', 'what is 10% of 800'), send it to the **Math Agent**.\n"
+        "- If the user uploaded a CSV file and asks about table content, data trends, or uses words like 'data', 'table', 'csv', 'column', or 'row', send it to the **CSV Agent**.\n"
+        "- If the user asks about current events, trending topics, or online information (e.g., 'What is LangChain?', 'latest news'), send it to the **Search Agent**.\n"
+        "- If none of these apply, use your best judgment to choose the most relevant agent."
+    ),
+    expected_output="The final answer from the selected agent or tool.",
+    agent=router_agent,
+    input_variables=["query"]
+)
+
+crew = Crew(
+    agents=[summarizer_agent, document_qa_agent, search_agent, math_agent, csv_agent],
+    tasks=[router_task],
+    process=Process.sequential,
+    verbose=True,
+    llm=crew_llm
+)
+
+def multi_agent_chat(query: str) -> str:
+    print(f"Routing query: {query}")
+    try:
+        result = crew.kickoff(inputs={"query": query})
+        result_str = str(result)
+        if "I don't know." in result_str or result_str.strip() == "":
+            return search_agent(query)  # fallback 給搜尋
+        return f"[Agent: {result.agent_name}]\n{result.output}" #result_str
+    except Exception as e:
+        return f"Error: {e}"
+
+def multi_agent_chat_advanced(query: str, file=None) -> str:
+    global session_retriever, session_qa_chain
+
+    # 判斷是否為與文件無關的查詢
+    non_doc_keywords = ["calculate", "sum", "date", "time", "how many", "how much", "weather", "temperature"]
+    use_file_chain = True
+    for kw in non_doc_keywords:
+        if kw in query.lower():
+            use_file_chain = False
+            break
+
+    if file is not None:
+        file_path = get_file_path(file)
+        if file_path is None:
+            return "Unable to process the file format."
+
+        # === CSV 處理 ===
+        if file_path.lower().endswith(".csv"):
+            global csv_dataframe
+            try:              
+                with open(file_path, 'rb') as f:
+                    result = chardet.detect(f.read())
+                    encoding = result['encoding']
+                df = pd.read_csv(file_path, encoding=encoding)
+                csv_dataframe = df
+                result = crew.kickoff(inputs={"query": query})
+                return f"[Agent: {result.agent_name}]\n{result.output}"
+            except Exception as e:
+                return f"Error reading CSV: {e}"
+
+        # === 文本類型文件（PDF / DOCX / TXT） ===
+        elif file_path.lower().endswith((".pdf", ".txt", ".docx")):
+            loader = (
+                PyPDFLoader(file_path) if file_path.lower().endswith(".pdf")
+                else UnstructuredWordDocumentLoader(file_path) if file_path.lower().endswith(".docx")
+                else TextLoader(file_path)
+            )
+            docs = loader.load()
+            chunks = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50).split_documents(docs)
+            db = FAISS.from_documents(chunks, embeddings)
+            session_retriever = db.as_retriever()
+            session_qa_chain = ConversationalRetrievalChain.from_llm(
+                llm=llm_gpt4,
+                retriever=session_retriever,
+                memory=ConversationBufferMemory(memory_key="chat_history", return_messages=True),
+                #combine_docs_chain_kwargs={"prompt": custom_prompt}
+            )
+
+            # 決定使用摘要還是 QA
+            if any(kw in query.lower() for kw in ["summarize", "summary", "摘要", "總結"]):
+                return document_summarize(file_path)
+            elif use_file_chain:
+                try:
+                    return session_qa_chain.run(query)
+                except Exception as e:
+                    return f"Error: {e}"
+            else:
+                try:
+                    result = crew.kickoff(inputs={"query": query})
+                    return f"[Agent: {result.agent_name}]\n{result.output}"
+                except Exception as e:
+                    return f"Error: {e}"
+
+        else:
+            return "Unsupported file format."
+
+    # 沒有上傳新檔案
+    elif session_qa_chain is not None:
+        if use_file_chain:
+            try:
+                return session_qa_chain.run(query)
+            except Exception as e:
+                return f"Error: {e}"
+        else:
+            try:
+                result = crew.kickoff(inputs={"query": query})
+                return f"[Agent: {result.agent_name}]\n{result.output}"
+            except Exception as e:
+                return f"Error: {e}"
+
+    # 沒有 session，直接丟給 CrewAI
+    else:
+        try:
+            result = crew.kickoff(inputs={"query": query})
+            return f"[Agent: {result.agent_name}]\n{result.output}"
+        except Exception as e:
+            return f"Error: {e}"
+
+# === Gradio Interface Settings ===
+demo_description = """
+**Context**:
+This demo uses a Retrieval-Augmented Generation (RAG) system based on 
+Biden’s 2023 State of the Union Address. 
+All responses are grounded in this document. 
+If no relevant information is found in the document, the system will say "No relevant info found."
+
+**Sample Questions**:
+1. What were the main topics regarding infrastructure in this speech?
+2. How does the speech address the competition with China?
+3. What does Biden say about job growth in the past two years?
+4. Does the speech mention anything about Social Security or Medicare?
+5. What does the speech propose regarding Big Tech or online privacy?
+
+*Note: The LLaMA module generates responses based solely on the current query without follow-up memory or chat history management.*
+
+Feel free to ask any question related to Biden’s 2023 State of the Union Address.
+"""
+demo_description2 = """
+**Context**:
+This demo uses a Retrieval-Augmented Generation (RAG) system based on 
+Biden’s 2023 State of the Union Address. 
+All responses are grounded in this document. 
+If no relevant information is found in the document, the system will say "No relevant info found."
+
+**Sample Questions**:
+1. What were the main topics regarding infrastructure in this speech?
+2. How does the speech address the competition with China?
+3. What does Biden say about job growth in the past two years?
+4. Does the speech mention anything about Social Security or Medicare?
+5. What does the speech propose regarding Big Tech or online privacy?
+
+*Note: The GPT module supports follow-up questions with conversation history management, enabling more interactive and context-aware discussions.*
+
+Feel free to ask any question related to Biden’s 2023 State of the Union Address.
+"""
+demo_description3 = """
+**Context**:
+Upload a PDF, TXT, or DOCX file and ask a question about its content.
+This demo uses GPT-4 to answer questions based on the content of your uploaded document.
+
+Feel free to ask any question related to your document.
+"""
+demo_description4 = """
+**Context**:
+This assistant performs multi-agent tasks using tools such as:
+- Document summarisation
+- FAQ-style document Q&A
+- Financial or CSV-style logic queries
+- Multi-step reasoning via agent orchestration
+
+The system will automatically select the appropriate function based on the uploaded file (CSV, PDF, TXT, DOCX) and the query content.
+For example, if the query contains "summarize"/"摘要", it will summarize the document; if it's CSV data, it will perform data analysis.
+"""
+demo_description5 = """
+**Context**:
+This demo uses Document Summarization via a Map-Reduce chain.
+Upload a PDF, TXT, or DOCX file to get an automatic summary of its contents.
+"""
+
+demo = gr.TabbedInterface(
+    interface_list=[
+        gr.Interface(
+            fn=rag_llama_qa,
+            inputs="text",
+            outputs="text",
+            title="Biden Q&A (LLaMA)",
+            allow_flagging="never",
+            description=demo_description
+        ),
+        gr.Interface(
+            fn=rag_gpt4_qa,
+            inputs="text",
+            outputs="text",
+            title="Biden Q&A (GPT-4)",
+            allow_flagging="never",
+            description=demo_description2
+        ),
+        gr.Interface(
+            fn=upload_and_chat,
+            inputs=[gr.File(label="Upload PDF, TXT, or DOCX"), gr.Textbox(label="Ask a question")],
+            outputs="text",
+            title="Your Docs Q&A (Upload + GPT-4)",
+            allow_flagging="never",
+            description=demo_description3
+        ),
+        gr.Interface(
+            fn=document_summarize,
+            inputs=[gr.File(label="Upload PDF, TXT, or DOCX")],
+            outputs="text",
+            title="Document Summarization",
+            allow_flagging="never",
+            description=demo_description5
+        ),
+        gr.Interface(
+            fn=multi_agent_chat_advanced,
+            inputs=[
+                gr.Textbox(label="請輸入查詢內容"),
+                gr.File(label="上傳文件 (CSV, PDF, TXT, DOCX)", file_count="single")
+            ],
+            outputs="text",
+            title="Multi-Agent AI Assistant",
+            allow_flagging="never",
+            description=demo_description4
+        )
+    ],
+    tab_names=[
+        "Biden Q&A (LLaMA)",
+        "Biden Q&A (GPT-4)",
+        "Your Docs Q&A (Upload + GPT-4)",
+        "Document Summarization",
+        "Multi-Agent AI Assistant"
+    ],
+    title="RAG + Multi-Agent Platform"
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)