Space: add langchain-community for PyPDFLoader

app.py

@@ -1,25 +1,30 @@
-# app.py  (repo root)
+import sys
+import os
 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")
+# day3 qovluğunu sys.path-ə əlavə et
+sys.path.append(os.path.join(os.path.dirname(__file__), "day3"))
 
-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"]
+from rag_system import RAGPipeline
+
+rag = RAGPipeline()
+
+def ask_pdf(pdf_file, question):
+    if pdf_file:
+        info = rag.index_document(pdf_file.name)
+    else:
+        info = {"chunks_indexed": 0, "best_strategy": None}
+    out = rag.query(question)
+    return f"**Answer:** {out['answer']}\n\n_Used chunks: {out['used_chunks']}_"
 
 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."
+    fn=ask_pdf,
+    inputs=[
+        gr.File(label="Upload PDF", file_types=[".pdf"]),
+        gr.Textbox(label="Question")
+    ],
+    outputs="markdown",
+    title="PDF RAG System"
 )
 
 if __name__ == "__main__":

requirements.txt

@@ -1,6 +0,0 @@
-gradio
-chromadb
-sentence-transformers
-langchain-groq
-pypdf
-python-dotenv