Create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+import os
+import requests
+
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+
+# ✅ Read OpenRouter API key from HF secret
+OPENROUTER_API_KEY = os.environ.get("ArjunHF")
+
+class OpenRouterChatModel(ChatOpenAI):
+    def __init__(self, **kwargs):
+        super().__init__(
+            openai_api_base="https://openrouter.ai/api/v1",
+            openai_api_key=OPENROUTER_API_KEY,
+            model_name="mistralai/mistral-small-3.2-24b-instruct:free",
+            **kwargs
+        )
+
+def qa_on_url(url, question):
+    try:
+        loader = WebBaseLoader(url)
+        docs = loader.load()
+        splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+        split_docs = splitter.split_documents(docs)
+
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        vectordb = FAISS.from_documents(split_docs, embeddings)
+        retriever = vectordb.as_retriever()
+
+        llm = OpenRouterChatModel(temperature=0.2)
+        qa_chain = RetrievalQA.from_chain_type(llm, retriever=retriever)
+        return qa_chain.run(question)
+    except Exception as e:
+        return f"❌ Error: {e}"
+
+iface = gr.Interface(
+    fn=qa_on_url,
+    inputs=[gr.Textbox(label="Enter Web URL"), gr.Textbox(label="Your Question")],
+    outputs="text",
+    title="🔎 Ask Questions About Any Webpage (Mistral 3.2 via OpenRouter + LangChain)",
+    description="⚠️ This may take 10–20 seconds depending on the page length and LLM response time. Please be patient!"
+)
+
+if __name__ == "__main__":
+    iface.launch()