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| import os | |
| from dotenv import load_dotenv | |
| import httpx | |
| import streamlit as st | |
| from langchain.prompts import PromptTemplate | |
| from langchain_huggingface import HuggingFaceEndpoint | |
| from langchain_core.messages import BaseMessage, HumanMessage | |
| from langgraph.graph import MessageGraph, END | |
| from typing import Sequence | |
| load_dotenv() | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| WEATHER_TOKEN = os.getenv("WEATHER_TOKEN") | |
| # streamlit app | |
| st.title("City Weather Information with AI Review") | |
| city = st.text_input("Enter the name of a city:") | |
| # Initialize the HuggingFace inference endpoint | |
| llm = HuggingFaceEndpoint( | |
| repo_id="mistralai/Mistral-7B-Instruct-v0.3", | |
| huggingfacehub_api_token=HF_TOKEN.strip(), | |
| temperature=0.7, | |
| max_new_tokens=200 | |
| ) | |
| # Define nodes | |
| def fetch_weather_node(state: Sequence[BaseMessage]) -> str: | |
| city = state[0].content.strip() | |
| url = f"https://api.openweathermap.org/data/2.5/weather?q={city}&appid={WEATHER_TOKEN}&units=metric" | |
| try: | |
| response = httpx.get(url) | |
| response.raise_for_status() | |
| weather_data = response.json() | |
| weather = weather_data['weather'][0]['main'] | |
| temperature = weather_data['main']['temp'] | |
| return f"The current weather in {city} is {weather} with a temperature of {temperature}°C." | |
| except Exception as e: | |
| return f"Error: {e}" | |
| def generate_review_node(state: Sequence[BaseMessage]) -> str: | |
| input_text = state[0].content | |
| response = llm(input_text) | |
| return response | |
| # Define the prompt template for generating weather reviews | |
| review_prompt_template = """ | |
| You are an expert weather analyst. Based on the provided weather information, generate a detailed and insightful review. | |
| Weather Information: {weather_info[1]} | |
| Your review should include an analysis of the weather conditions and finish in 100 words. | |
| Review: | |
| """ | |
| # Create and configure the graph | |
| builder = MessageGraph() | |
| # Add nodes | |
| builder.add_node("fetch_weather", fetch_weather_node) | |
| builder.add_node("generate_review", generate_review_node) | |
| builder.set_entry_point("fetch_weather") | |
| # Define transitions | |
| builder.add_edge("fetch_weather", "generate_review") | |
| builder.set_finish_point("generate_review") | |
| # Compile the graph | |
| graph = builder.compile() | |
| # Streamlit app | |
| if st.button("Get Weather Information and Review"): | |
| if city: | |
| with st.spinner("Processing..."): | |
| try: | |
| # Prepare the input for the graph | |
| weather_info = graph.invoke(HumanMessage(content=city)) | |
| st.write(weather_info[1].content) | |
| # Generate the review using the refined prompt | |
| review_input = review_prompt_template.format(weather_info=weather_info) | |
| review = graph.invoke(HumanMessage(content=review_input)) | |
| st.subheader("AI Generated Weather Review") | |
| st.write(review[2].content) | |
| st.subheader("Mermaid Graph") | |
| st.write("Check out this [mermaid link](https://mermaid.live/) to display a graph with following data") | |
| mermaid_code = graph.get_graph().draw_mermaid() | |
| st.markdown( | |
| f""" | |
| ```mermaid | |
| {mermaid_code} | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| except Exception as e: | |
| st.error(f"Error generating weather review: {e}") | |
| else: | |
| st.warning("Please enter a city name.") | |