Create app.py

app.py

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+import streamlit as st
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad import format_to_openai_functions
+import wikipedia
+from langchain.tools.render import format_tool_to_openai_function
+from langchain.prompts import MessagesPlaceholder
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from pydantic import Field,BaseModel
+from langchain.agents import tool
+from dotenv import load_dotenv
+import requests
+from pydantic import BaseModel, Field
+import datetime
+load_dotenv()
+
+st.set_page_config(page_title="Chatbot", page_icon="💬")
+st.header('Agent Chatbot')
+st.write('Allows users to interact with the ChatGPT,Wikipedia and fetch current weather temperature of any place.')
+
+chat_model = ChatOpenAI(model="gpt-3.5-turbo-1106",max_tokens=50,streaming=True)
+
+# Define the input schema
+class OpenMeteoInput(BaseModel):
+    latitude: float = Field(..., description="Latitude of the location to fetch weather data for")
+    longitude: float = Field(..., description="Longitude of the location to fetch weather data for")
+
+@tool(args_schema=OpenMeteoInput)
+def get_current_temperature(latitude: float, longitude: float) -> dict:
+    """Fetch current temperature for given coordinates."""
+    
+    BASE_URL = "https://api.open-meteo.com/v1/forecast"
+    
+    # Parameters for the request
+    params = {
+        'latitude': latitude,
+        'longitude': longitude,
+        'hourly': 'temperature_2m',
+        'forecast_days': 1,
+    }
+
+    # Make the request
+    response = requests.get(BASE_URL, params=params)
+    
+    if response.status_code == 200:
+        results = response.json()
+    else:
+        return f"API Request failed with status code: {response.status_code}"
+
+    current_utc_time = datetime.datetime.utcnow()
+    time_list = [datetime.datetime.fromisoformat(time_str.replace('Z', '+00:00')) for time_str in results['hourly']['time']]
+    temperature_list = results['hourly']['temperature_2m']
+    
+    closest_time_index = min(range(len(time_list)), key=lambda i: abs(time_list[i] - current_utc_time))
+    current_temperature = temperature_list[closest_time_index]
+    
+    return f'The current temperature is {current_temperature}°C'
+
+
+@tool
+def SearchWikiPedia(query: str):
+    "Serach wikipedia and get page summaries."
+
+    page_titles = wikipedia.search(query)
+    
+    summaries = []
+
+    for page_title in page_titles[: 3]:
+        try:
+
+            wiki_page = wikipedia.page(title=page_title,auto_suggest=False)
+
+            summaries.append(f"Page: {wiki_page.title}\nSummary: {wiki_page.summary}")
+
+        except Exception:
+            pass
+
+    if not summaries:
+        return "No Good WikiPedia Search results found."
+    return "\n\n".join(summaries)
+
+tools =  [get_current_temperature,SearchWikiPedia]
+
+functions = [format_tool_to_openai_function(t) for t in tools]
+model_with_function = chat_model.bind(functions = functions)
+
+
+prompt = ChatPromptTemplate.from_messages([("system","You are helfull assistant."),
+    MessagesPlaceholder(variable_name="chat_history"),
+    ("user","{user_input}"),
+    MessagesPlaceholder(variable_name="agent_scratchpad")])
+
+chain = prompt | model_with_function | OpenAIFunctionsAgentOutputParser()
+
+agent_chain = RunnablePassthrough.assign(
+    agent_scratchpad = lambda x: format_to_openai_functions(x['intermediate_steps'])
+) | chain
+
+# memory = ConversationBufferWindowMemory(return_messages=True,memory_key="chat_history",k=2)
+if "chat_history" not in st.session_state:
+    st.session_state["chat_history"] = ConversationBufferWindowMemory(return_messages=True,memory_key="chat_history",k=2)
+
+agent_Executor = AgentExecutor(agent=agent_chain,tools=tools,memory=st.session_state["chat_history"])
+
+# Streamlit code
+from langchain.schema import HumanMessage,AIMessage
+if user_input:= st.chat_input("HI: Please Enter some thing"):
+    response = agent_Executor.invoke({'user_input':user_input})
+    for msg in st.session_state["chat_history"].chat_memory.messages:
+        if isinstance(msg,HumanMessage):
+            st.chat_message("user").write(msg.content)
+        else:
+            st.chat_message("assistant").write(msg.content)