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| import streamlit as st | |
| import requests | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # Load Hugging Face Model | |
| tokenizer = AutoTokenizer.from_pretrained("unsloth/Llama-3.2-1B", trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-1B", trust_remote_code=True) | |
| def generate_text(prompt): | |
| inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=1024) | |
| with torch.no_grad(): | |
| outputs = model.generate(**inputs, max_length=1024, pad_token_id=tokenizer.eos_token_id) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Function to fetch Wikipedia summary | |
| def search_travel_info(destination): | |
| url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{destination}" | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| data = response.json() | |
| return data.get("extract", "No information found.") | |
| return "No results found." | |
| # Function to generate travel itinerary | |
| def generate_itinerary(start_location, budget, duration, destination, purpose, preferences): | |
| search_results = search_travel_info(destination) | |
| # System Prompt | |
| system_prompt = "You are an expert travel guide. Your goal is to create a well-structured, detailed itinerary based on the user's preferences." | |
| # User Prompt | |
| user_prompt = f""" | |
| {system_prompt} | |
| ### 🏷️ **Traveler Information**: | |
| - **Budget**: {budget} | |
| - **Purpose of Travel**: {purpose} | |
| - **Preferences**: {preferences} | |
| ### 🚆 **Day-wise Itinerary**: | |
| - 📍 Day-by-day activities, including morning, afternoon, and evening plans | |
| - 🎭 Must-visit attractions (famous landmarks + hidden gems) | |
| - 🍽️ Local cuisines and top dining recommendations | |
| - 🏨 Best places to stay (based on budget) | |
| - 🚗 Transportation options (from {start_location} to {destination} and local travel) | |
| ### 📌 **Additional Considerations**: | |
| - 🌎 Cultural experiences, festivals, or seasonal events | |
| - 🛍️ Shopping and souvenir recommendations | |
| - 🔹 Safety tips, best times to visit, and local customs | |
| - 🗺️ Alternative plans for bad weather days | |
| ### ℹ️ **Additional Information from External Sources**: | |
| {search_results} | |
| Make sure the itinerary is engaging, practical, and customized based on the user’s budget and preferences. | |
| """ | |
| # Generate Response | |
| return generate_text(user_prompt) | |
| # Streamlit UI | |
| st.title("AI-Powered Travel Planner") | |
| st.write("Plan your next trip with AI!") | |
| start_location = st.text_input("Starting Location") | |
| destination = st.text_input("Destination") | |
| budget = st.selectbox("Select Budget", ["Low", "Moderate", "Luxury"]) | |
| duration = st.number_input("Trip Duration (days)", min_value=1, max_value=30, value=3) | |
| purpose = st.text_area("Purpose of Trip") | |
| preferences = st.text_area("Your Preferences (e.g., adventure, food, history)") | |
| if st.button("Generate Itinerary"): | |
| if start_location and destination and purpose and preferences: | |
| itinerary = generate_itinerary(start_location, budget, duration, destination, purpose, preferences) | |
| st.subheader("Your AI-Generated Itinerary:") | |
| st.write(itinerary) | |
| else: | |
| st.warning("Please fill in all fields.") | |