Update app.py

app.py

@@ -61,18 +61,25 @@ if uploaded_file:
         text_splitter = RecursiveCharacterTextSplitter(chunk_size=256, chunk_overlap=50)
         chunked_documents = text_splitter.split_documents(documents)
 
-        # Generate embeddings
-        HF_token = "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"
-        embeddings = HuggingFaceInferenceAPIEmbeddings(api_key=HF_token, model_name="BAAI/bge-base-en-v1.5")
+        # Ensure the chunked documents list is not empty
+        if not chunked_documents:
+            st.error("No content extracted from the document.")
+        else:
+            # Generate embeddings
+            HF_token = "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"
+            embeddings = HuggingFaceInferenceAPIEmbeddings(api_key=HF_token, model_name="BAAI/bge-base-en-v1.5")
 
-        # Create a vector store
-        vectorstore = Chroma.from_documents(chunked_documents, embeddings)
-        retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 3})
+            # Create a vector store
+            try:
+                vectorstore = Chroma.from_documents(chunked_documents, embeddings)
+                retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 3})
 
-        # User query
-        query = st.text_input("Enter your query:")
+                # User query
+                query = st.text_input("Enter your query:")
 
-        if query:
-            response = answer_with_retrieval(query, retriever)
-            st.write("### Response")
-            st.write(response)
+                if query:
+                    response = answer_with_retrieval(query, retriever)
+                    st.write("### Response")
+                    st.write(response)
+            except Exception as e:
+                st.error(f"Error creating vector store or generating embeddings: {str(e)}")