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import os
from dotenv import load_dotenv
import httpx
import gradio as gr
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 environment variables
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")
WEATHER_TOKEN = os.getenv("WEATHER_TOKEN")
# 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(city: str) -> str:
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(weather_info: str) -> str:
response = llm(weather_info)
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}
Your review should include an analysis of the weather conditions and finish in 150 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()
# Define the Gradio interface
def get_weather_and_review(city: str) -> str:
if city:
try:
# Prepare the input for the graph
weather_info = graph.invoke(HumanMessage(content=city))
weather_info_text = weather_info[1].content
# Generate the review using the refined prompt
review_input = review_prompt_template.format(weather_info=weather_info_text)
review = graph.invoke(HumanMessage(content=review_input))
review_text = review[2].content
return f"**Weather Information:**\n{weather_info_text}\n\n**AI Generated Weather Review:**\n{review_text}"
except Exception as e:
return f"Error generating weather review: {e}"
else:
return "Please enter a city name."
interface = gr.Interface(
fn=get_weather_and_review,
inputs=gr.Textbox(lines=2, placeholder="Enter the name of a city:", label="City"),
outputs="text",
title="City Weather Information with AI Review",
description="Enter the name of a city to get current weather information and an AI-generated review based on that information."
)
if __name__ == "__main__":
interface.launch()
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