Create app.py
Browse files
app.py
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from typing import TypedDict, Annotated, List, Union
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from langchain_core.messages import HumanMessage, AIMessage
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_groq import ChatGroq
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import requests
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import gradio as gr
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class PlannerState(TypedDict):
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messages: Annotated[List[Union[HumanMessage, AIMessage]], "The messages in the conversation"]
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city: str
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interests: List[str]
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itinerary: str
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# Initialize the LLM with better engagement
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llm = ChatGroq(
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temperature=0.7,
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groq_api_key="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",
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model_name="llama-3.3-70b-versatile"
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)
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# Prompt for generating an itinerary
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itinerary_prompt = ChatPromptTemplate.from_messages([
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("system", "You are a smart travel agent who creates engaging, fun, and optimized day trip itineraries for {city}. \
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Tailor recommendations based on the user's interests: {interests}. \
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Include hidden gems, famous spots, and local cuisine. Keep it structured, with timestamps."),
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("human", "Plan my perfect day trip!")
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])
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# Prompt for generating a fun fact
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fun_fact_prompt = ChatPromptTemplate.from_messages([
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("system", "You are a knowledgeable travel guide. Share an interesting and unique fun fact about {city} that most travelers don’t know."),
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("human", "Tell me a fun fact about {city}!")
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])
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# Function to collect city input
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def input_city(city: str, state: PlannerState) -> PlannerState:
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return {
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**state,
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"city": city,
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"messages": state["messages"] + [HumanMessage(content=f"City: {city}")]
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}
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# Function to collect interests input
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def input_interests(interests: str, state: PlannerState) -> PlannerState:
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interest_list = [interest.strip() for interest in interests.split(",")]
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return {
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**state,
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"interests": interest_list,
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"messages": state["messages"] + [HumanMessage(content=f"Interests: {', '.join(interest_list)}")]
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}
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# Function to create the itinerary
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def create_itinerary(state: PlannerState) -> str:
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response = llm.invoke(itinerary_prompt.format_messages(city=state["city"], interests=', '.join(state["interests"])))
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return response.content
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# Function to fetch real-time weather data
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def get_weather(city: str) -> str:
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api_key = "b787841c42e84b32b70235141251102"
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url = f"http://api.weatherapi.com/v1/current.json?key={api_key}&q={city}&aqi=no"
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try:
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response = requests.get(url)
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data = response.json()
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if "current" in data:
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weather_desc = data["current"]["condition"]["text"]
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temp = data["current"]["temp_c"]
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return f"Current weather in {city}: {weather_desc}, {temp}°C"
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else:
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return "Weather data unavailable."
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except:
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return "Error fetching weather."
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# Function to generate a fun fact about the city
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def fun_fact(city: str) -> str:
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response = llm.invoke(fun_fact_prompt.format_messages(city=city))
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return response.content
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# Gradio function to generate travel plan
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def travel_planner(city: str, interests: str):
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# Initialize state
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state = {
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"messages": [],
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"city": "",
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"interests": [],
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"itinerary": "",
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}
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# Update state with user inputs
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state = input_city(city, state)
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state = input_interests(interests, state)
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# Generate itinerary, weather, and fun fact
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itinerary = create_itinerary(state)
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weather = get_weather(city)
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fact = fun_fact(city)
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return itinerary, weather, fact
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# Build the Gradio interface with enhanced UI
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interface = gr.Interface(
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fn=travel_planner,
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inputs=[
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gr.Textbox(label="Enter the city for your trip", placeholder="e.g., Paris, Tokyo, New York"),
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gr.Textbox(label="Enter your interests (comma-separated)", placeholder="e.g., history, food, adventure"),
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],
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outputs=[
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gr.Textbox(label="Generated Itinerary", interactive=False),
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gr.Textbox(label="Current Weather", interactive=False),
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gr.Textbox(label="Fun Fact", interactive=False)
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],
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theme="soft",
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title="✈️ AI Travel Planner",
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description="Plan your next adventure! Get a custom itinerary, weather forecast, and a fun fact!",
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live=True,
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)
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# Launch the Gradio application
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interface.launch()
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