update app.py

app.py

@@ -10,8 +10,8 @@ from langchain.document_loaders import PyPDFLoader
 from langchain.document_loaders import TextLoader
 from langchain.document_loaders import Docx2txtLoader
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from dotenv import load_dotenv  # Add this line for loading environment variables
 import os
+from dotenv import load_dotenv
 import tempfile
 
 load_dotenv()
@@ -36,22 +36,27 @@ def display_chat_history(chain):
     container = st.container()
 
     with container:
-        col1, col2 = st.columns(2)
+        with st.form(key='my_form', clear_on_submit=True):
+            user_input = st.text_input("Question:", placeholder="Ask about your Documents", key='input')
+            submit_button = st.form_submit_button(label='Send')
 
-        with col1:
-            with st.form(key='my_form', clear_on_submit=True):
-                user_input = st.text_input("Question:", placeholder="Ask about your Documents", key='input')
-                submit_button = st.form_submit_button(label='Send')
+        if submit_button and user_input:
+            with st.spinner('Generating response...'):
+                output = conversation_chat(user_input, chain, st.session_state['history'])
 
-        with col2:
-            if st.session_state['generated']:
-                for i in range(len(st.session_state['generated'])):
-                    message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs")
-                    message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
+            st.session_state['past'].append(user_input)
+            st.session_state['generated'].append(output)
+
+    if st.session_state['generated']:
+        with reply_container:
+            for i in range(len(st.session_state['generated'])):
+                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs")
+                message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
 
 def create_conversational_chain(vector_store):
     load_dotenv()
-    replicate_api_token = "r8_AA3K1fhDykqLa5M74E5V0w5ss1z0P9S3foWJl"  # Replace with your actual token
+
+    replicate_api_token = "r8_AA3K1fhDykqLa5M74E5V0w5ss1z0P9S3foWJl"
     os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
 
     llm = Replicate(
@@ -98,8 +103,6 @@ def main():
         text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=100, length_function=len)
         text_chunks = text_splitter.split_documents(text)
 
-        st.write("Text chunks lengths:", [len(chunk) for chunk in text_chunks])
-
         embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
                                            model_kwargs={'device': 'cpu'})
         vector_store = FAISS.from_documents(text_chunks, embedding=embeddings)