Create app.py

app.py

@@ -0,0 +1,67 @@
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_community.chat_models.huggingface import ChatHuggingFace
+from composio_langchain import ComposioToolset, App
+from langchain import hub
+from langchain.agents import AgentExecutor, load_tools
+from langchain.agents.format_scratchpad import format_log_to_str
+from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser
+from langchain.tools.render import render_text_description
+from langchain_community.utilities import SerpAPIWrapper
+import os
+import gradio as gr
+
+!composio-cli add github
+!composio-cli add gmail
+!composio-cli add notion
+
+
+os.environ["SERPAPI_API_KEY"] = os.getenv("SERPAPI_API_KEY")
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+def setup_llm(repo_id):
+    return HuggingFaceEndpoint(repo_id=repo_id)
+
+def setup_chat_model(llm):
+    return ChatHuggingFace(llm=llm)
+
+def setup_tools(llm):
+    return load_tools(["serpapi", "llm-math", "stackexchange"], llm=llm)
+
+def setup_prompt(tools):
+    prompt = hub.pull("hwchase17/react-json")
+    prompt = prompt.partial(tools=render_text_description(tools),
+                            tool_names=", ".join([t.name for t in tools]))
+    return prompt
+
+def setup_agent(chat_model_with_stop, tools, prompt):
+    return (
+        {
+            "input": lambda x: x["input"],
+            "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
+        }
+        | prompt
+        | chat_model_with_stop
+        | ReActJsonSingleInputOutputParser()
+    )
+
+def execute_agent(agent, tools, input_text):
+    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)
+    agent_executor.return_intermediate_steps = True
+    return agent_executor.invoke({"input": input_text})
+
+llm = setup_llm(repo_id="HuggingFaceH4/zephyr-7b-beta")
+chat_model = setup_chat_model(llm)
+tools = ComposioToolset(apps=[App.GITHUB, App.GMAIL])
+tools.extend(setup_tools(llm))
+prompt = setup_prompt(tools)
+chat_model_with_stop = chat_model.bind(stop=["\nInvalidStop"])
+agent = setup_agent(chat_model_with_stop, tools, prompt)
+
+def response(input,history=[]):
+  res = execute_agent(agent, tools, input)
+  if(res["intermediate_steps"]==[]):
+    return res["output"]
+  else:
+    return res["intermediate_steps"][0]+"\n"+res["output"]
+
+
+gr.ChatInterface(response).launch(share=True, debug=True)