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app (1).py

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+# app.py  (repo root)
+import gradio as gr
+from day3.rag_system import RAGPipeline
+
+# separate persistent dir for Spaces
+rag = RAGPipeline(persist_dir="./chroma_db_space", collection_name="pdf_docs")
+
+def chat_with_pdf(pdf_file, question):
+    if pdf_file is None or not question:
+        return "Please upload a PDF and enter a question."
+    # index uploaded PDF (idempotent for small demos)
+    rag.index_document(pdf_file.name, doc_id_prefix="upload")
+    out = rag.query(question, k=4)
+    return out["answer"]
+
+demo = gr.Interface(
+    fn=chat_with_pdf,
+    inputs=[gr.File(label="Upload PDF", file_types=[".pdf"]),
+            gr.Textbox(label="Ask a question")],
+    outputs=gr.Textbox(label="Answer"),
+    title="PDF RAG (Chroma + Groq)",
+    description="Upload a PDF, ask a question. Uses Chroma for retrieval and Groq LLM for answering."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements (1).txt

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+gradio
+chromadb
+sentence-transformers
+langchain-groq
+pypdf
+python-dotenv