Create app-original.py

app-original.py

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+import os
+import requests
+from langchain.agents import Tool
+from langchain.tools import BaseTool
+from langchain.agents import load_tools
+from langchain.memory import ConversationBufferMemory
+from langchain.memory import ConversationBufferWindowMemory
+#from langchain.chat_models import ChatOpenAI
+from langchain.utilities import GoogleSearchAPIWrapper
+from langchain.utilities import GoogleSerperAPIWrapper
+from langchain.agents import initialize_agent
+import gradio as gr 
+from langchain.chains.question_answering import load_qa_chain
+from langchain import PromptTemplate, LLMChain
+from langchain import HuggingFaceHub
+from pathlib import Path
+from time import sleep
+from langchain.agents import AgentType
+#from langchain.llms import OpenAI
+
+from langchain.agents import AgentOutputParser
+from langchain.agents.conversational_chat.prompt import FORMAT_INSTRUCTIONS
+from langchain.output_parsers.json import parse_json_markdown
+from langchain.schema import AgentAction, AgentFinish
+
+import os
+import random
+import string
+from dotenv import load_dotenv
+load_dotenv()
+
+from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
+from langchain import OpenAI, SerpAPIWrapper, LLMChain, LLMMathChain
+     
+
+OPENAI_API_KEY =os.getenv("OPENAI_API_KEY")
+GOOGLE_API_KEY =os.getenv("GOOGLE_API_KEY")
+GOOGLE_CSE_ID =os.getenv("GOOGLE_CSE_ID")
+#HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
+#repo_id = os.getenv('repo_id')
+HUGGINGFACEHUB_API_TOKEN = os.environ.get('HUGGINGFACEHUB_API_TOKEN')
+SERPAPI_API_KEY=os.environ.get('SERPAPI_API_KEY')
+repo_id = os.environ.get('repo_id')
+
+template = """Question: {question}
+
+Answer: Let's think step by step."""
+
+prompt_lora = PromptTemplate(template=template, input_variables=["question"])
+
+llm=HuggingFaceHub(repo_id=repo_id)
+
+lora_chain = LLMChain(prompt=prompt_lora,llm = llm)
+
+question = "How many fish can live in a ocean that is as big as North America?"
+
+print(lora_chain.run(question))
+
+# This is an LLMChain to write a synopsis given a title of a play.
+
+template = """You are a engineer. Given the title, it is your job to write a synopsis for that title.
+
+Title: {title}
+Engineer: This is a synopsis for the above title:"""
+
+prompt_template = PromptTemplate(input_variables=["title"], 
+                                 template=template)
+
+synopsis_chain = LLMChain(llm=llm, 
+                          prompt=prompt_template)
+
+template = """You are a MBA from Harvard. Given the synopsis, it is your job to write a review for that synopsis.
+
+Synopsis:
+{synopsis}
+Review from a Harvard MBA of above synopsis:"""
+
+prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)
+
+review_chain = LLMChain(llm=llm, prompt=prompt_template)
+
+from langchain.chains import SimpleSequentialChain
+overall_chain = SimpleSequentialChain(chains=[synopsis_chain, review_chain], verbose=True)
+
+overall_chain.run("How big is London Bridge?")
+
+
+search = SerpAPIWrapper()
+llm_math_chain = LLMMathChain(llm=llm, verbose=True)
+
+tools = [
+    Tool(
+        name = "Search",
+        func=search.run,
+        description="useful for when you need to answer questions about current events"
+    ),
+    Tool(
+        name="Calculator",
+        func=llm_math_chain.run,
+        description="useful for when you need to answer questions about Divide, Multiply, Add and Subtract"
+    )
+]
+
+
+prefix = """Answer the following questions as best you can. Think through step by step.
+You have access to the following tools:"""
+suffix = """Begin! Remember to think step by step
+
+Question: {input}
+{agent_scratchpad}"""
+
+prompt = ZeroShotAgent.create_prompt(
+    tools, 
+    prefix=prefix, 
+    suffix=suffix, 
+    input_variables=["input", "agent_scratchpad"]
+)
+
+agent_chain = LLMChain(llm=llm, prompt=prompt)
+
+tool_names = [tool.name for tool in tools]
+agent = ZeroShotAgent(llm_chain=agent_chain, 
+                      allowed_tools=tool_names)
+
+agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, 
+                                                    tools=tools, 
+                                                    verbose=True,
+                                                    max_iterations=3)
+
+agent.save("custom_agent.json")
+
+result=agent_executor.run("How many people live in canada as of 2023?")
+
+print("Result: "+str(result))