Rename cowen.py to app.py

cowen.py → app.py

@@ -1,325 +1,323 @@
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.chat_models import ChatOpenAI
-from langchain_openai import AzureChatOpenAI,AzureOpenAIEmbeddings
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationChain
-from langchain.chains import ConversationalRetrievalChain
-from langchain.document_loaders import UnstructuredFileLoader
-from typing import List, Dict, Tuple
-import gradio as gr
-import validators
-import requests
-import mimetypes
-import tempfile
-import os
-from langchain.chains.question_answering import load_qa_chain
-from langchain.llms import OpenAI
-from langchain.prompts import PromptTemplate
-from langchain.prompts.prompt import PromptTemplate
-import pandas as pd
-from langchain_experimental.agents.agent_toolkits import create_csv_agent
-from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
-from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
-from langchain.agents.agent_types import AgentType
-# from langchain.agents import create_csv_agent
-from langchain import OpenAI, LLMChain
-from openai import AzureOpenAI
-
-os.environ['AZURE_OPENAI_API_KEY'] = "a96a965049c8420dad412abf07cbd26d"
-os.environ['AZURE_OPENAI_ENDPOINT'] = "https://eastus2.api.cognitive.microsoft.com/"
-os.environ['OPENAI_API_VERSION'] = "2024-02-01"
-
-class ChatDocumentQA:
-    def __init__(self) -> None:
-        pass
-
-    def _get_empty_state(self) -> Dict[str, None]:
-        """Create an empty knowledge base."""
-        return {"knowledge_base": None}
-
-    def _extract_text_from_pdfs(self, file_paths: List[str]) -> List[str]:
-        """Extract text content from PDF files.
-
-        Args:
-            file_paths (List[str]): List of file paths.
-
-        Returns:
-            List[str]: Extracted text from the PDFs.
-        """
-        docs = []
-        loaders = [UnstructuredFileLoader(file_obj, strategy="fast") for file_obj in file_paths]
-        for loader in loaders:
-            docs.extend(loader.load())
-        return docs
-
-    def _get_content_from_url(self, urls: str) -> List[str]:
-        """Fetch content from given URLs.
-
-        Args:
-            urls (str): Comma-separated URLs.
-
-        Returns:
-            List[str]: List of text content fetched from the URLs.
-        """
-        file_paths = []
-        for url in urls.split(','):
-            if validators.url(url):
-                headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',}
-                r = requests.get(url, headers=headers)
-                if r.status_code != 200:
-                    raise ValueError("Check the url of your file; returned status code %s" % r.status_code)
-                content_type = r.headers.get("content-type")
-                file_extension = mimetypes.guess_extension(content_type)
-                temp_file = tempfile.NamedTemporaryFile(suffix=file_extension, delete=False)
-                temp_file.write(r.content)
-                file_paths.append(temp_file.name)
-
-        print("File_Paths:",file_paths)
-        docs = self._extract_text_from_pdfs(file_paths)
-        return docs
-
-    def _split_text_into_chunks(self, text: str) -> List[str]:
-        """Split text into smaller chunks.
-
-        Args:
-            text (str): Input text to be split.
-
-        Returns:
-            List[str]: List of smaller text chunks.
-        """
-        text_splitter = CharacterTextSplitter(separator="\n", chunk_size=6000, chunk_overlap=0, length_function=len)
-
-        chunks = text_splitter.split_documents(text)
-
-        return chunks
-
-    def _create_vector_store_from_text_chunks(self, text_chunks: List[str]) -> FAISS:
-        """Create a vector store from text chunks.
-
-        Args:
-            text_chunks (List[str]): List of text chunks.
-
-        Returns:
-            FAISS: Vector store created from the text chunks.
-        """
-        embeddings = AzureOpenAIEmbeddings(
-                        azure_deployment="text-embedding-3-large",
-                    )
-
-        return FAISS.from_documents(documents=text_chunks, embedding=embeddings)
-
-
-    def _create_conversation_chain(self,vectorstore):
-
-        _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
-
-        Chat History:  {chat_history}
-        Follow Up Input: {question}
-        Standalone question:"""
-        CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
-
-        memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
-        # llm = ChatOpenAI(temperature=0)
-        llm=AzureChatOpenAI(azure_deployment = "GPT-4o")
-
-        return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(),
-                                                     condense_question_prompt=CONDENSE_QUESTION_PROMPT,
-                                                     memory=memory)
-
-    def _get_documents_knowledge_base(self, file_paths: List[str]) -> Tuple[str, Dict[str, FAISS]]:
-        """Build knowledge base from uploaded files.
-
-        Args:
-            file_paths (List[str]): List of file paths.
-
-        Returns:
-            Tuple[str, Dict]: Tuple containing a status message and the knowledge base.
-        """
-        file_path = file_paths[0].name
-        file_extension = os.path.splitext(file_path)[1]
-
-        if file_extension == '.csv':
-            # agent = self.create_agent(file_path)
-            # tools = self.get_agent_tools(agent)
-            # memory,tools,prompt = self.create_memory_for_csv_qa(tools)
-            # agent_chain = self.create_agent_chain_for_csv_qa(memory,tools,prompt)
-            agent_chain = create_csv_agent(
-                          AzureChatOpenAI(azure_deployment = "GPT-4o"),
-                          file_path,
-                          verbose=True,
-                          agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-            )
-            return "file uploaded", {"knowledge_base": agent_chain}
-
-        else:
-            pdf_docs = [file_path.name for file_path in file_paths]
-            raw_text = self._extract_text_from_pdfs(pdf_docs)
-            text_chunks = self._split_text_into_chunks(raw_text)
-            vectorstore = self._create_vector_store_from_text_chunks(text_chunks)
-            return "file uploaded", {"knowledge_base": vectorstore}
-
-
-    def _get_urls_knowledge_base(self, urls: str) -> Tuple[str, Dict[str, FAISS]]:
-        """Build knowledge base from URLs.
-
-        Args:
-            urls (str): Comma-separated URLs.
-
-        Returns:
-            Tuple[str, Dict]: Tuple containing a status message and the knowledge base.
-        """
-        webpage_text = self._get_content_from_url(urls)
-        text_chunks = self._split_text_into_chunks(webpage_text)
-        vectorstore = self._create_vector_store_from_text_chunks(text_chunks)
-        return "file uploaded", {"knowledge_base": vectorstore}
-
-#************************
-#   csv qa
-#************************
-    def create_agent(self,file_path):
-        agent_chain = create_csv_agent(
-        AzureChatOpenAI(azure_deployment = "GPT-4o"),
-        file_path,
-        verbose=True,
-        agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-        )
-        return agent_chain
-    def get_agent_tools(self,agent):
-      # search = agent
-      tools = [
-        Tool(
-            name="dataframe qa",
-            func=agent.run,
-            description="useful for when you need to answer questions about table data and dataframe data",
-        )
-      ]
-      return tools
-
-    def create_memory_for_csv_qa(self,tools):
-      prefix = """Have a conversation with a human, answering the following questions about table data and dataframe data as best you can. You have access to the following tools:"""
-      suffix = """Begin!"
-
-      {chat_history}
-      Question: {input}
-      {agent_scratchpad}"""
-
-      prompt = ZeroShotAgent.create_prompt(
-        tools,
-        prefix=prefix,
-        suffix=suffix,
-        input_variables=["input", "chat_history", "agent_scratchpad"],
-      )
-      memory = ConversationBufferMemory(memory_key="chat_history",return_messages=True)
-
-      return memory,tools,prompt
-
-    def create_agent_chain_for_csv_qa(self,memory,tools,prompt):
-
-        llm_chain = LLMChain(llm=AzureChatOpenAI(azure_deployment = "GPT-4o"), prompt=prompt)
-        agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
-        agent_chain = AgentExecutor.from_agent_and_tools(
-            agent=agent, tools=tools, verbose=True, memory=memory
-        )
-
-        return agent_chain
-
-    def _get_response(self, message: str, chat_history: List[Tuple[str, str]], state: Dict[str, FAISS],file_paths) -> Tuple[str, List[Tuple[str, str]]]:
-        """Get a response from the chatbot.
-
-        Args:
-            message (str): User's message/question.
-            chat_history (List[Tuple[str, str]]): List of chat history as tuples of (user_message, bot_response).
-            state (dict): State containing the knowledge base.
-
-        Returns:
-            Tuple[str, List[Tuple[str, str]]]: Tuple containing a status message and updated chat history.
-        """
-        try:
-          if file_paths:
-            file_path = file_paths[0].name
-            file_extension = os.path.splitext(file_path)[1]
-
-            if file_extension == '.csv':
-                agent_chain = state["knowledge_base"]
-                response = agent_chain.run(input = message)
-                chat_history.append((message, response))
-                return "", chat_history
-
-            else:
-                vectorstore = state["knowledge_base"]
-                chat = self._create_conversation_chain(vectorstore)
-                response = chat({"question": message,"chat_history": chat_history})
-                chat_history.append((message, response["answer"]))
-                return "", chat_history
-          else:
-                vectorstore = state["knowledge_base"]
-                chat = self._create_conversation_chain(vectorstore)
-                response = chat({"question": message,"chat_history": chat_history})
-                chat_history.append((message, response["answer"]))
-                return "", chat_history
-        except:
-            chat_history.append((message, "Please Upload Document or URL"))
-            return "", chat_history
-
-    def gradio_interface(self) -> None:
-        """Create a Gradio interface for the chatbot."""
-        with gr.Blocks(css="#textbox_id textarea {color: white}",theme='SherlockRamos/Feliz') as demo:
-            gr.HTML("""
-              <style>
-                .footer {
-                    display: none !important;
-                }
-                footer {
-                    display: none !important;
-                }
-                #foot {
-                    display: none !important;
-                }
-                .svelte-1fzp3xt {
-                    display: none !important;
-                }
-                #root > div > div > div {
-                    padding-bottom: 0 !important;
-                }
-                .custom-footer {
-                    text-align: center;
-                    padding: 10px;
-                    font-size: 14px;
-                    color: #333;
-                }
-            </style>
-            """)
-            gr.HTML("""<div><img src="data:image/jpeg;base64,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" alt="Intercontinental Exchange" style="float:left;width:80px;height:80px;"><h1 style="color:#000;margin-left:4in;padding-top:10px">Virtual Assistant Chatbot</h1></div>""")
-            state = gr.State(self._get_empty_state())
-            chatbot = gr.Chatbot()
-
-            with gr.Row():
-                with gr.Column(scale=0.85):
-                    msg = gr.Textbox(label="Question", elem_id="textbox_id")
-                with gr.Column(scale=0.15):
-                    file_output = gr.Textbox(label="File Status")
-            with gr.Row():
-                with gr.Column(scale=0.85):
-                    clear = gr.ClearButton([msg, chatbot])
-                with gr.Column(scale=0.15):
-                    upload_button = gr.UploadButton(
-                        "Browse File",
-                        file_types=[".txt", ".pdf", ".doc", ".docx", ".csv"],
-                        file_count="multiple", variant="primary"
-                    )
-            with gr.Row():
-                with gr.Column(scale=1):
-                    input_url = gr.Textbox(label="urls", elem_id="textbox_id")
-
-            input_url.submit(self._get_urls_knowledge_base, input_url, [file_output, state])
-            upload_button.upload(self._get_documents_knowledge_base, upload_button, [file_output, state])
-            msg.submit(self._get_response, [msg, chatbot, state,upload_button], [msg, chatbot])
-
-        demo.launch(debug=True,allowed_paths=["/content/"])
-
-
-if __name__ == "__main__":
-    chatdocumentqa = ChatDocumentQA()
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain_openai import AzureChatOpenAI,AzureOpenAIEmbeddings
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationChain
+from langchain.chains import ConversationalRetrievalChain
+from langchain.document_loaders import UnstructuredFileLoader
+from typing import List, Dict, Tuple
+import gradio as gr
+import validators
+import requests
+import mimetypes
+import tempfile
+import os
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain.prompts.prompt import PromptTemplate
+import pandas as pd
+from langchain_experimental.agents.agent_toolkits import create_csv_agent
+from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
+from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
+from langchain.agents.agent_types import AgentType
+# from langchain.agents import create_csv_agent
+from langchain import OpenAI, LLMChain
+from openai import AzureOpenAI
+
+
+
+class ChatDocumentQA:
+    def __init__(self) -> None:
+        pass
+
+    def _get_empty_state(self) -> Dict[str, None]:
+        """Create an empty knowledge base."""
+        return {"knowledge_base": None}
+
+    def _extract_text_from_pdfs(self, file_paths: List[str]) -> List[str]:
+        """Extract text content from PDF files.
+
+        Args:
+            file_paths (List[str]): List of file paths.
+
+        Returns:
+            List[str]: Extracted text from the PDFs.
+        """
+        docs = []
+        loaders = [UnstructuredFileLoader(file_obj, strategy="fast") for file_obj in file_paths]
+        for loader in loaders:
+            docs.extend(loader.load())
+        return docs
+
+    def _get_content_from_url(self, urls: str) -> List[str]:
+        """Fetch content from given URLs.
+
+        Args:
+            urls (str): Comma-separated URLs.
+
+        Returns:
+            List[str]: List of text content fetched from the URLs.
+        """
+        file_paths = []
+        for url in urls.split(','):
+            if validators.url(url):
+                headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',}
+                r = requests.get(url, headers=headers)
+                if r.status_code != 200:
+                    raise ValueError("Check the url of your file; returned status code %s" % r.status_code)
+                content_type = r.headers.get("content-type")
+                file_extension = mimetypes.guess_extension(content_type)
+                temp_file = tempfile.NamedTemporaryFile(suffix=file_extension, delete=False)
+                temp_file.write(r.content)
+                file_paths.append(temp_file.name)
+
+        print("File_Paths:",file_paths)
+        docs = self._extract_text_from_pdfs(file_paths)
+        return docs
+
+    def _split_text_into_chunks(self, text: str) -> List[str]:
+        """Split text into smaller chunks.
+
+        Args:
+            text (str): Input text to be split.
+
+        Returns:
+            List[str]: List of smaller text chunks.
+        """
+        text_splitter = CharacterTextSplitter(separator="\n", chunk_size=6000, chunk_overlap=0, length_function=len)
+
+        chunks = text_splitter.split_documents(text)
+
+        return chunks
+
+    def _create_vector_store_from_text_chunks(self, text_chunks: List[str]) -> FAISS:
+        """Create a vector store from text chunks.
+
+        Args:
+            text_chunks (List[str]): List of text chunks.
+
+        Returns:
+            FAISS: Vector store created from the text chunks.
+        """
+        embeddings = AzureOpenAIEmbeddings(
+                        azure_deployment="text-embedding-3-large",
+                    )
+
+        return FAISS.from_documents(documents=text_chunks, embedding=embeddings)
+
+
+    def _create_conversation_chain(self,vectorstore):
+
+        _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
+
+        Chat History:  {chat_history}
+        Follow Up Input: {question}
+        Standalone question:"""
+        CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+
+        memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+        # llm = ChatOpenAI(temperature=0)
+        llm=AzureChatOpenAI(azure_deployment = "GPT-4o")
+
+        return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(),
+                                                     condense_question_prompt=CONDENSE_QUESTION_PROMPT,
+                                                     memory=memory)
+
+    def _get_documents_knowledge_base(self, file_paths: List[str]) -> Tuple[str, Dict[str, FAISS]]:
+        """Build knowledge base from uploaded files.
+
+        Args:
+            file_paths (List[str]): List of file paths.
+
+        Returns:
+            Tuple[str, Dict]: Tuple containing a status message and the knowledge base.
+        """
+        file_path = file_paths[0].name
+        file_extension = os.path.splitext(file_path)[1]
+
+        if file_extension == '.csv':
+            # agent = self.create_agent(file_path)
+            # tools = self.get_agent_tools(agent)
+            # memory,tools,prompt = self.create_memory_for_csv_qa(tools)
+            # agent_chain = self.create_agent_chain_for_csv_qa(memory,tools,prompt)
+            agent_chain = create_csv_agent(
+                          AzureChatOpenAI(azure_deployment = "GPT-4o"),
+                          file_path,
+                          verbose=True,
+                          agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+            )
+            return "file uploaded", {"knowledge_base": agent_chain}
+
+        else:
+            pdf_docs = [file_path.name for file_path in file_paths]
+            raw_text = self._extract_text_from_pdfs(pdf_docs)
+            text_chunks = self._split_text_into_chunks(raw_text)
+            vectorstore = self._create_vector_store_from_text_chunks(text_chunks)
+            return "file uploaded", {"knowledge_base": vectorstore}
+
+
+    def _get_urls_knowledge_base(self, urls: str) -> Tuple[str, Dict[str, FAISS]]:
+        """Build knowledge base from URLs.
+
+        Args:
+            urls (str): Comma-separated URLs.
+
+        Returns:
+            Tuple[str, Dict]: Tuple containing a status message and the knowledge base.
+        """
+        webpage_text = self._get_content_from_url(urls)
+        text_chunks = self._split_text_into_chunks(webpage_text)
+        vectorstore = self._create_vector_store_from_text_chunks(text_chunks)
+        return "file uploaded", {"knowledge_base": vectorstore}
+
+#************************
+#   csv qa
+#************************
+    def create_agent(self,file_path):
+        agent_chain = create_csv_agent(
+        AzureChatOpenAI(azure_deployment = "GPT-4o"),
+        file_path,
+        verbose=True,
+        agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        )
+        return agent_chain
+    def get_agent_tools(self,agent):
+      # search = agent
+      tools = [
+        Tool(
+            name="dataframe qa",
+            func=agent.run,
+            description="useful for when you need to answer questions about table data and dataframe data",
+        )
+      ]
+      return tools
+
+    def create_memory_for_csv_qa(self,tools):
+      prefix = """Have a conversation with a human, answering the following questions about table data and dataframe data as best you can. You have access to the following tools:"""
+      suffix = """Begin!"
+
+      {chat_history}
+      Question: {input}
+      {agent_scratchpad}"""
+
+      prompt = ZeroShotAgent.create_prompt(
+        tools,
+        prefix=prefix,
+        suffix=suffix,
+        input_variables=["input", "chat_history", "agent_scratchpad"],
+      )
+      memory = ConversationBufferMemory(memory_key="chat_history",return_messages=True)
+
+      return memory,tools,prompt
+
+    def create_agent_chain_for_csv_qa(self,memory,tools,prompt):
+
+        llm_chain = LLMChain(llm=AzureChatOpenAI(azure_deployment = "GPT-4o"), prompt=prompt)
+        agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
+        agent_chain = AgentExecutor.from_agent_and_tools(
+            agent=agent, tools=tools, verbose=True, memory=memory
+        )
+
+        return agent_chain
+
+    def _get_response(self, message: str, chat_history: List[Tuple[str, str]], state: Dict[str, FAISS],file_paths) -> Tuple[str, List[Tuple[str, str]]]:
+        """Get a response from the chatbot.
+
+        Args:
+            message (str): User's message/question.
+            chat_history (List[Tuple[str, str]]): List of chat history as tuples of (user_message, bot_response).
+            state (dict): State containing the knowledge base.
+
+        Returns:
+            Tuple[str, List[Tuple[str, str]]]: Tuple containing a status message and updated chat history.
+        """
+        try:
+          if file_paths:
+            file_path = file_paths[0].name
+            file_extension = os.path.splitext(file_path)[1]
+
+            if file_extension == '.csv':
+                agent_chain = state["knowledge_base"]
+                response = agent_chain.run(input = message)
+                chat_history.append((message, response))
+                return "", chat_history
+
+            else:
+                vectorstore = state["knowledge_base"]
+                chat = self._create_conversation_chain(vectorstore)
+                response = chat({"question": message,"chat_history": chat_history})
+                chat_history.append((message, response["answer"]))
+                return "", chat_history
+          else:
+                vectorstore = state["knowledge_base"]
+                chat = self._create_conversation_chain(vectorstore)
+                response = chat({"question": message,"chat_history": chat_history})
+                chat_history.append((message, response["answer"]))
+                return "", chat_history
+        except:
+            chat_history.append((message, "Please Upload Document or URL"))
+            return "", chat_history
+
+    def gradio_interface(self) -> None:
+        """Create a Gradio interface for the chatbot."""
+        with gr.Blocks(css="#textbox_id textarea {color: white}",theme='SherlockRamos/Feliz') as demo:
+            gr.HTML("""
+              <style>
+                .footer {
+                    display: none !important;
+                }
+                footer {
+                    display: none !important;
+                }
+                #foot {
+                    display: none !important;
+                }
+                .svelte-1fzp3xt {
+                    display: none !important;
+                }
+                #root > div > div > div {
+                    padding-bottom: 0 !important;
+                }
+                .custom-footer {
+                    text-align: center;
+                    padding: 10px;
+                    font-size: 14px;
+                    color: #333;
+                }
+            </style>
+            """)
+            gr.HTML("""<div><img src="data:image/jpeg;base64,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" alt="Intercontinental Exchange" style="float:left;width:80px;height:80px;"><h1 style="color:#000;margin-left:4in;padding-top:10px">Virtual Assistant Chatbot</h1></div>""")
+            state = gr.State(self._get_empty_state())
+            chatbot = gr.Chatbot()
+
+            with gr.Row():
+                with gr.Column(scale=0.85):
+                    msg = gr.Textbox(label="Question", elem_id="textbox_id")
+                with gr.Column(scale=0.15):
+                    file_output = gr.Textbox(label="File Status")
+            with gr.Row():
+                with gr.Column(scale=0.85):
+                    clear = gr.ClearButton([msg, chatbot])
+                with gr.Column(scale=0.15):
+                    upload_button = gr.UploadButton(
+                        "Browse File",
+                        file_types=[".txt", ".pdf", ".doc", ".docx", ".csv"],
+                        file_count="multiple", variant="primary"
+                    )
+            with gr.Row():
+                with gr.Column(scale=1):
+                    input_url = gr.Textbox(label="urls", elem_id="textbox_id")
+
+            input_url.submit(self._get_urls_knowledge_base, input_url, [file_output, state])
+            upload_button.upload(self._get_documents_knowledge_base, upload_button, [file_output, state])
+            msg.submit(self._get_response, [msg, chatbot, state,upload_button], [msg, chatbot])
+
+        demo.launch(debug=True,allowed_paths=["/content/"])
+
+
+if __name__ == "__main__":
+    chatdocumentqa = ChatDocumentQA()
     chatdocumentqa.gradio_interface()