Create app.py

app.py

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+import os
+import gradio as gr
+from langchain.document_loaders import WebBaseLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.chains import RetrievalQA
+from langchain_together import Together
+
+# 🔐 Set your API key
+os.environ["TOGETHER_API_KEY"] = os.environ.get("TOGETHER_API_KEY", "")
+
+
+# 🔁 Caches
+qa_cache = {}
+retriever_cache = {}
+
+# 🔍 Load and embed the website content
+def load_url(url):
+    try:
+        loader = WebBaseLoader(url)
+        docs = loader.load()
+        splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+        chunks = splitter.split_documents(docs)
+        embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+        db = FAISS.from_documents(chunks, embedding=embeddings)
+        retriever = db.as_retriever()
+        llm = Together(
+            model="mistralai/Mistral-7B-Instruct-v0.2",
+            temperature=0.5,
+            max_tokens=512
+        )
+        qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
+        return retriever, qa, "✅ Website loaded. You can start chatting!"
+    except Exception as e:
+        return None, None, f"❌ Error: {str(e)}"
+
+# 💬 Chat handler
+def chat(message, history, url):
+    if url not in qa_cache:
+        retriever, qa, status = load_url(url)
+        if retriever is None:
+            history.append({"role": "user", "content": message})
+            history.append({"role": "assistant", "content": status})
+            return history, ""
+        retriever_cache[url] = retriever
+        qa_cache[url] = qa
+        history.append({"role": "system", "content": status})
+    else:
+        qa = qa_cache[url]
+
+    try:
+        result = qa.invoke({"query": message})["result"]
+    except Exception as e:
+        result = f"❌ Error: {str(e)}"
+
+    history.append({"role": "user", "content": message})
+    history.append({"role": "assistant", "content": result})
+    return history, ""
+
+# ✅ Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 Chat with Any Website")
+
+    url_input = gr.Textbox(label="Website URL", placeholder="https://en.wikipedia.org/wiki/LangChain")
+    chatbot = gr.Chatbot(label="Chat", type="messages")
+    msg_input = gr.Textbox(show_label=False, placeholder="Ask your question here and press Enter...")
+    state = gr.State([])
+
+    msg_input.submit(chat, inputs=[msg_input, state, url_input], outputs=[chatbot, msg_input])
+
+    # 👇 Footer
+    gr.Markdown(
+        """
+        ---
+        <center>
+            🔗 <a href="https://github.com/vivekreddy1105" target="_blank">GitHub</a> | 
+            💼 <a href="https://www.linkedin.com/in/vivekreddy1105/" target="_blank">LinkedIn</a><br>
+            © 2025 Vivek Reddy Eluka
+        </center>
+        """,
+        elem_id="footer"
+    )
+
+demo.launch(share=True)