Update app.py

app.py

@@ -5,14 +5,16 @@ from langchain_huggingface import HuggingFaceEmbeddings, HuggingFaceEndpoint
 from langchain_community.llms import HuggingFaceHub
 from langchain.chains import ConversationalRetrievalChain
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.memory import ConversationBufferMemory
-from langchain_community.document_loaders import PyPDFLoader
+from langchain.memory   
+ import ConversationBufferMemory
+from langchain_community.document_loaders   
+ import PyPDFLoader
 import os
+
 load_dotenv()
 
 # Get the Hugging Face API token from the .env file
 hf_api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-
 if hf_api_token is None:
     raise ValueError("HUGGINGFACEHUB_API_TOKEN not found in .env file")
 
@@ -22,7 +24,8 @@ embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-Mi
 # Initialize the LLaMA 2 model from Hugging Face Hub using the token from .env
 llm = HuggingFaceEndpoint(
     repo_id="meta-llama/Llama-2-7b-hf",
-    model_kwargs={"temperature": 0.7, "max_length": 512},
+    temperature=0.7,  # Specify temperature explicitly
+    max_length=512,
     huggingfacehub_api_token=hf_api_token
 )
 
@@ -31,7 +34,9 @@ vectorstore = Chroma(embedding_function=embedding_model, persist_directory="chro
 
 # Create a conversational chain with retrieval capabilities
 memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-qa_chain = ConversationalRetrievalChain.from_llm(llm, retriever=vectorstore.as_retriever(), memory=memory)
+qa_chain = ConversationalRetrievalChain.from_llm(llm,   
+ retriever=vectorstore.as_retriever(), memory=memory)   
+
 
 def upload_docs(docs):
     # Load and process the uploaded PDF documents
@@ -42,12 +47,12 @@ def upload_docs(docs):
 
     # Split documents into manageable chunks
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    texts = text_splitter.split_documents(loaded_docs)
+    texts = text_splitter.split_documents(loaded_docs)   
+
 
     # Add documents to the vector store and persist them
     vectorstore.add_documents(texts)
     vectorstore.persist()
-
     return "PDF documents uploaded and processed successfully!"
 
 def chat(query):
@@ -62,11 +67,10 @@ with gr.Blocks() as demo:
             doc_upload = gr.File(label="Upload your PDF documents", file_types=[".pdf"], multiple=True)
             upload_button = gr.Button("Upload")
             upload_button.click(upload_docs, inputs=doc_upload, outputs=gr.Textbox())
-
         with gr.Column():
             chat_input = gr.Textbox(label="Ask a question:")
             chat_output = gr.Textbox(label="Answer:")
             chat_button = gr.Button("Send")
             chat_button.click(chat, inputs=chat_input, outputs=chat_output)
 
-demo.launch()
+demo.launch()