Update app.py

app.py

@@ -30,53 +30,43 @@ load_dotenv()
 def main():
     st.header('Chat With PDF')
     
-    pdf = st.file_uploader('Upload Your PDF',type='pdf')
+    pdf = st.file_uploader('Upload Your PDF', type='pdf')
+
+if pdf is not None:
+    pdf_reader = PdfReader(pdf)
+    
+    text = ''
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+    
+    chunks = text_splitter.split_text(text=text)
+    
+    store_name = pdf.name[:-4]
+    
+    if os.path.exists(f'{store_name}.pkl'):
+        with open(f'{store_name}.pkl', 'rb') as f:
+            VectorStore = pickle.load(f)
+    else:
+        embeddings = HuggingFaceEmbeddings()
+        VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
+        with open(f'{store_name}.pkl', 'wb') as f:
+            pickle.dump(VectorStore, f)
+    
+    ask_query = st.text_input('Ask question about PDF: ')
     
-    if pdf is not None:
-        pdf_reader = PdfReader(pdf)
-        # st.write(pdf_reader)
+    if ask_query:
+        docs = VectorStore.similarity_search(query=ask_query, k=3)
+        llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0, "max_length": 64})
+        chain = load_qa_chain(llm=llm, chain_type='stuff')
+        response = chain.run(input_documents=docs, question=ask_query)
+        st.write(response)
 
-        text = ''
-        for page in pdf_reader.pages:
-            text = page.extract_text()
-            
-            text_splitter = RecursiveCharacterTextSplitter(
-                chunk_size = 1000,
-                chunk_overlap = 200,
-                length_function = len
-            )
-            
-            chunks = text_splitter.split_text(text=text)
-            
-            # st.write(chunks)
-            
-            # embeddings
-            
-            store_name = pdf.name[:-4]
-            
-            if os.path.exists(f'{store_name}.pkl'):
-                with open(f'{store_name}.pkl','rb') as f:
-                    VectorStore = pickle.load(f)
-            else:
-                embeddings = HuggingFaceEmbeddings()
-                VectorStore = FAISS.from_texts(chunks,embedding=embeddings)
-                with open(f'{store_name}.pkl','wb') as f:
-                    pickle.dump(VectorStore,f)
-                    
-                    
-            # accept user query's
-            
-            ask_query = st.text_input('Ask question about PDF : ')
-            
-            
-            if ask_query:
-                docs = VectorStore.similarity_search(query=ask_query, k=3)
-                # st.write(docs)
-                llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0, "max_length": 64})
-                chain = load_qa_chain(llm=llm, chain_type='stuff')
-                response = chain.run(input_documents=docs, question=ask_query)
-                st.write(response)
-            # st.write(text)