app.py

# app.py

import os
from dotenv import load_dotenv
from tavily import TavilyClient
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# 1. Load environment variables from .env file (if present)
load_dotenv()

# 2. Set API keys from environment
GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
TAVILY_API_KEY = os.environ.get("TAVILY_API_KEY")

# 3. Initialize Gemini LLM
llm = ChatGoogleGenerativeAI(
    model="models/gemini-1.5-flash",
    google_api_key=GOOGLE_API_KEY
)

# 4. Tavily Client
tavily_client = TavilyClient(api_key=TAVILY_API_KEY)

def extract_website_text(url):
    result = tavily_client.extract(urls=url)
    if result and "text" in result[0]:
        return result[0]["text"]
    return "Could not extract content from the URL."

# 5. Prompt Template
prompt = PromptTemplate(
    input_variables=["website_content", "question"],
    template="""
You are an intelligent assistant. Based on the following website content:

{website_content}

Answer the following question:
{question}
"""
)

# 6. LLM QA Chain
qa_chain = LLMChain(llm=llm, prompt=prompt)

# 7. Main Chat Function
def ask_from_website(url, question):
    print(f"\n🔗 Extracting content from: {url}")
    website_text = extract_website_text(url)
    
    print(f"\n💬 Asking: {question}")
    response = qa_chain.invoke({
        "website_content": website_text,
        "question": question
    })

    print("\n✅ Answer:")
    print(response["text"])

# 8. Run
if __name__ == "__main__":
    url = input("Enter the website URL: ")
    question = input("What do you want to ask about this website? ")
    ask_from_website(url, question)