Create app.py

app.py

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+# chatbot_url_query.py
+
+import os
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.retrievers import TavilyRetriever
+from langchain.chains import RetrievalQA
+
+# 1. Set API keys (Replace with your actual keys)
+os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
+os.environ["TAVILY_API_KEY"] = "your-tavily-api-key"
+
+# 2. Load Google Gemini model
+llm = ChatGoogleGenerativeAI(
+    model="models/gemini-1.5-flash",
+    google_api_key=os.environ["GOOGLE_API_KEY"]
+)
+
+# 3. Initialize Tavily Retriever (fetches data from web)
+retriever = TavilyRetriever(k=3)
+
+# 4. Create Retrieval QA Chain
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    retriever=retriever,
+    return_source_documents=True
+)
+
+# 5. Function to run the chatbot
+def ask_web_query(url, question):
+    query = f"Given the content of the website {url}, answer the following: {question}"
+    response = qa_chain.invoke({"query": query})
+    
+    print("\n✅ Answer:")
+    print(response["result"])
+
+    print("\n📄 Sources:")
+    for doc in response["source_documents"]:
+        print("-", doc.metadata.get("source", "Unknown"))
+
+# 6. Run your chatbot
+if __name__ == "__main__":
+    url = input("Enter website URL: ")
+    question = input("Ask your question about the website: ")
+    ask_web_query(url, question)