Update app.py

app.py

@@ -51,33 +51,31 @@ else:
     pdf_path = None
 
 # Step 2: Process PDF
-if pdf_path:
-    with st.spinner("Loading PDF..."):
-        loader = PDFPlumberLoader(pdf_path)
-        docs = loader.load()
+with st.spinner("Loading PDF..."):
+    loader = PDFPlumberLoader(pdf_path)
+    docs = loader.load()
     
-    st.success(f"✅ **PDF Loaded!** Total Pages: {len(docs)}")
-
-    # Step 3: Chunking
-    with st.spinner("Chunking the document..."):
-        model_name = "nomic-ai/modernbert-embed-base"
-        embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'})
-
-        text_splitter = SemanticChunker(embedding_model)
-        documents = text_splitter.split_documents(docs)
-
-    st.success(f"✅ **Document Chunked!** Total Chunks: {len(documents)}")
-
-    # Step 4: Setup Vectorstore
-    with st.spinner("Creating vector store..."):
-        vector_store = Chroma(
-            collection_name="deepseek_collection",
-            collection_metadata={"hnsw:space": "cosine"},
-            embedding_function=embedding_model
-        )
-        vector_store.add_documents(documents)
-
-    st.success("✅ **Vector Store Created!**")
+st.success(f"✅ **PDF Loaded!** Total Pages: {len(docs)}")
+
+# Step 3: Chunking
+with st.spinner("Chunking the document..."):
+    model_name = "nomic-ai/modernbert-embed-base"
+    embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'})
+    text_splitter = SemanticChunker(embedding_model)
+    documents = text_splitter.split_documents(docs)
+
+st.success(f"✅ **Document Chunked!** Total Chunks: {len(documents)}")
+
+# Step 4: Setup Vectorstore
+with st.spinner("Creating vector store..."):
+    vector_store = Chroma(
+        collection_name="deepseek_collection",
+        collection_metadata={"hnsw:space": "cosine"},
+        embedding_function=embedding_model
+    )
+    vector_store.add_documents(documents)
+
+st.success("✅ **Vector Store Created!**")
 
     # Step 5: Query Input
     query = st.text_input("🔍 Enter a Query:")