Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import streamlit as st
 # from dotenv import load_dotenv
 import pickle
@@ -16,85 +15,93 @@ from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddi
 from langchain.embeddings import HuggingFaceHubEmbeddings
 # Sidebar contents
 from langchain.llms import HuggingFaceHub
+
+
+if 'HuggingFace_API_Key' not in st.session_state:
+    st.session_state['HuggingFace_API_Key'] = ''
+
 with st.sidebar:
     st.title('🤗💬 LLM Chat App')
     st.markdown('''
    ## About
-   This app is an LLM-powered chatbot built using:
-   - [Streamlit](https://streamlit.io/)
-   - [LangChain](https://python.langchain.com/)
-   - [OpenAI](https://platform.openai.com/docs/models) LLM model
+   This app is an LLM-powered chatbot
+   PDF:Chatbot AI-powered chat assistant for PDFs
 
    ''')
     add_vertical_space(5)
-    st.write(
-        'Made with ❤️ by [Prompt Engineer](https://youtube.com/@engineerprompt)')
+
+
+st.session_state['HuggingFace_API_Key'] = st.sidebar.text_input(
+    "What's your HuggingFace API key?", type="password")
 
 # load_dotenv()
 
-os.environ['OPEN_AI_APIKEY'] = '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'
+load_button = st.sidebar.button("Load data to Pinecone", key="load_button")
 
 
 def main():
     st.header("Chat with PDF 💬")
-
-    # upload a PDF file
     pdf = st.file_uploader("Upload your PDF", type='pdf')
 
-    # st.write(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)
-
-        # # embeddings
-        store_name = pdf.name[:-4]
-        st.write(f'{store_name}')
-        # st.write(chunks)
-
-        if os.path.exists(f"{store_name}.pkl"):
-            with open(f"{store_name}.pkl", "rb") as f:
-                VectorStore = pickle.load(f)
-            # st.write('Embeddings Loaded from the Disk')s
+    if st.session_state['HuggingFace_API_Key'] != "":
+        # upload a PDF file
+        # st.write(pdf)
+        if pdf is not None:
+            pdf_reader = PdfReader(pdf)
+
+            text = ""
+            for page in pdf_reader.pages:
+                text += page.extract_text()
+            # st.write(text)
+            text_splitter = RecursiveCharacterTextSplitter(
+                chunk_size=1000,
+                chunk_overlap=200,
+                length_function=len
+            )
+            chunks = text_splitter.split_text(text=text)
+
+            # # embeddings
+            store_name = pdf.name[:-4]
+            st.write(f'{store_name}')
+            # st.write(chunks)
+
+            if os.path.exists(f"{store_name}.pkl"):
+                with open(f"{store_name}.pkl", "rb") as f:
+                    VectorStore = pickle.load(f)
+                # st.write('Embeddings Loaded from the Disk')s
+            else:
+                # embeddings = OpenAIEmbeddings(
+                #     openai_api_key='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')
+                # embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+                embeddings = HuggingFaceHubEmbeddings(
+                    huggingfacehub_api_token=st.session_state['HuggingFace_API_Key'])
+                VectorStore = FAISS.from_texts(
+                    chunks, embedding=embeddings)
+                # VectorStore=Chroma.from_documents(chunks, embeddings)
+                with open(f"{store_name}.pkl", "wb") as f:
+                    pickle.dump(VectorStore, f)
+
+            # embeddings = OpenAIEmbeddings()
+            # VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
+
+            # Accept user questions/query
+            query = st.text_input("Ask questions about your PDF file:")
+            # st.write(query)
+
+            if query:
+                docs = VectorStore.similarity_search(query=query, k=3)
+                llm = HuggingFaceHub(repo_id='google/flan-ul2',
+                                     huggingfacehub_api_token=st.session_state['HuggingFace_API_Key'], model_kwargs={"temperature": 0.1, "max_new_tokens": 500})
+                # llm = OpenAI()
+                chain = load_qa_chain(llm=llm, chain_type="stuff")
+                response = chain.run(input_documents=docs, question=query)
+                # with get_openai_callback() as cb:
+                #     response = chain.run(input_documents=docs, question=query)
+                #     print(cb)
+                st.write(response)
         else:
-            # embeddings = OpenAIEmbeddings(
-            #     openai_api_key='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')
-            # embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
-            embeddings = HuggingFaceHubEmbeddings(
-                huggingfacehub_api_token='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')
-            VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
-            # VectorStore=Chroma.from_documents(chunks, embeddings)
-            with open(f"{store_name}.pkl", "wb") as f:
-                pickle.dump(VectorStore, f)
-
-        # embeddings = OpenAIEmbeddings()
-        # VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
-
-        # Accept user questions/query
-        query = st.text_input("Ask questions about your PDF file:")
-        # st.write(query)
-
-        if query:
-            docs = VectorStore.similarity_search(query=query, k=3)
-            llm = HuggingFaceHub(repo_id='google/flan-ul2',
-                                 huggingfacehub_api_token='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', model_kwargs={"temperature": 0.1, "max_new_tokens": 500})
-            # llm = OpenAI()
-            chain = load_qa_chain(llm=llm, chain_type="stuff")
-            response = chain.run(input_documents=docs, question=query)
-            # with get_openai_callback() as cb:
-            #     response = chain.run(input_documents=docs, question=query)
-            #     print(cb)
-            st.write(response)
+            st.sidebar.error("Ooopssss!!! Please upload pdf...")
 
 
 if __name__ == '__main__':
-    main()
+    main()