Create app.py

app.py

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+import os
+import gradio as gr
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatGroq
+from tempfile import NamedTemporaryFile
+
+# Load Groq API Key securely (for Hugging Face secrets)
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
+
+# Helper to process uploaded PDFs and build vectorstore
+def process_pdfs(files):
+    all_docs = []
+    for file in files:
+        with NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
+            temp_file.write(file.read())
+            loader = PyPDFLoader(temp_file.name)
+            all_docs.extend(loader.load())
+
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    chunks = splitter.split_documents(all_docs)
+
+    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    vectorstore = FAISS.from_documents(chunks, embeddings)
+    retriever = vectorstore.as_retriever()
+
+    llm = ChatGroq(model_name="mixtral-8x7b-32768", temperature=0)
+    qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
+    return qa_chain
+
+# Global chain (reset with new uploads)
+qa_chain = None
+
+# Upload + Process PDFs
+def upload_pdfs(files):
+    global qa_chain
+    qa_chain = process_pdfs(files)
+    return "✅ PDFs uploaded and processed. Now ask your questions."
+
+# Ask a question
+def ask_question(query):
+    if qa_chain is None:
+        return "❌ Please upload Kaggle notebooks/competition PDFs first."
+    result = qa_chain.run(query)
+    return result
+
+# Gradio UI
+upload = gr.File(file_types=[".pdf"], file_count="multiple", label="Upload Kaggle PDFs")
+btn_upload = gr.Button("Process PDFs")
+question = gr.Textbox(label="Ask a question about uploaded notebooks")
+answer = gr.Textbox(label="Assistant Answer")
+
+with gr.Blocks() as app:
+    gr.Markdown("## 🤖 Kaggle Study Assistant\nUpload PDFs from Kaggle and ask intelligent questions.")
+    upload_output = gr.Textbox(visible=True)
+    btn_upload.click(fn=upload_pdfs, inputs=upload, outputs=upload_output)
+    question.submit(fn=ask_question, inputs=question, outputs=answer)
+
+app.launch()