Create app1.py

app1.py

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+import gradio as gr
+from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
+from langchain.agents import Tool
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
+from langchain.chat_models import ChatOpenAI
+from langchain.agents import initialize_agent
+from langchain import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.schema import HumanMessage
+from langchain.callbacks.base import BaseCallbackHandler
+from threading import Thread
+from queue import Queue, Empty
+from threading import Thread
+from collections.abc import Generator
+
+class QueueCallback(BaseCallbackHandler):
+    def __init__(self, q):
+        self.q = q
+
+    def on_llm_new_token(self, token: str, **kwargs: any) -> None:
+        self.q.put(token)
+
+    def on_llm_end(self, *args, **kwargs: any) -> None:
+        return self.q.empty()
+
+PREFIX = ''' 
+        You are an Automobile expert AI scientist having all the knowledge about all the existing cars and bikes with their respective models and all the information around it.
+        If the question is not related to cars, bikes, automobiles or their related models then please let the user know that you don't have the relevant information.
+
+        Return the entire output in an HTML format.
+
+        Make sure to follow each and every instructions before giving the response.
+'''
+SUFFIX = '''
+        Begin!
+        Previous conversation history:
+        {chat_history}
+        Instructions: {input}
+        {agent_scratchpad}
+'''
+
+index = GPTSimpleVectorIndex.load_from_disk('./cars_bikes(2).json')
+tools = [
+        Tool(
+            name = "LlamaIndex",
+            func=lambda q: str(index.query(q)),
+            description="""You are an Automobile expert equipped with all the information related to all the existing cars, bikes and all its respective brands & models, features, parameters and specifications
+                          who is capable of perfectly answering everything related to every automobile brands in a tabular format or list.
+                          Answer using formatted tables or lists as when required.
+                          If the question is not related to cars, bikes, automobiles or their related models then please let the user know that you don't have the relevant information.
+                          Please answer keeping in mind the Indian context.
+
+                          Return the entire output in an HTML format.
+
+                          Make sure to follow each and every instructions before giving the response.
+                          """,
+            return_direct=True),
+    ]
+
+num_outputs = 2000
+conversational_memory = ConversationBufferWindowMemory( memory_key='chat_history', k=5, return_messages=True )
+llm = OpenAI(temperature=0.5, model_name="gpt-4",max_tokens=num_outputs)
+
+def stream(input_text) -> Generator:
+    conversation = initialize_agent(tools, llm, agent="conversational-react-description", memory=conversational_memory,agent_kwargs={'prefix':PREFIX,'suffix': SUFFIX})
+    
+    # Create a funciton to call - this will run in a thread
+    def task():
+        resp = conversation.run(input_text)
+        q.put(job_done)
+
+    # Create a thread and start the function
+    t = Thread(target=task)
+    t.start()
+
+    content = ""
+
+    # Get each new token from the queue and yield for our generator
+    while True:
+        try:
+            next_token = q.get(True, timeout=1)
+            if next_token is job_done:
+                break
+            content += next_token
+            yield next_token, content
+        except Empty:
+            continue
+
+add = "Return the output in a table format or an ordered list legible to the user.\n"
+def greet(Question):
+    for next_token, content in stream(Question):
+        yield(add+Question)
+
+demo = gr.Interface(
+    fn=greet,
+    inputs=gr.Textbox(lines=2, label="Question", placeholder="What do you want to know...?"),
+    outputs=gr.HTML(""),
+    title="Here Auto",
+    description="Know everything about Cars and Bikes",
+)
+demo.launch()