Update main.py

main.py

@@ -1,105 +1,106 @@
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-import os
-from langchain_community.vectorstores import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from dotenv import load_dotenv
-from langchain_openai import OpenAI, ChatOpenAI
-from langchain_openai import OpenAIEmbeddings
-
-load_dotenv()
-os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
-
-def get_pdf_text(pdf_docs):
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            try:
-                page_text = page.extract_text()
-                if page_text:
-                    text += page_text
-            except Exception as e:
-                print(f"Error reading page: {e}")
-    return text
-
-def get_text_chunks(text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2500, chunk_overlap=750)
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-def get_vector_store(text_chunks):
-    vector_store = FAISS.from_texts(text_chunks, OpenAIEmbeddings())
-    vector_store.save_local("faiss_index")
-
-def get_conversational_chain():
-    prompt_template = """You are an assistant for teachers. Your objective is to provide
-    comprehensive and accurate responses based on the context provided. Make sure that 
-    you generate whole output.
-    context: {context}
-    question: {question}
-    """
-
-    model = ChatOpenAI(model="gpt-3.5-turbo")
-    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
-    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-
-    return chain
-
-def user_input(user_question):
-    embeddings = OpenAIEmbeddings()
-    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-    docs = new_db.similarity_search(user_question)
-
-    chain = get_conversational_chain()
-    result = ""
-    with st.spinner("Processing..."):
-        response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
-        result = response["output_text"]
-    
-    st.session_state.chat_history.append({"role": "assistant", "content": result})
-
-def main():
-    st.set_page_config("Chat PDF")
-    st.header("Lesson Plan Maker With AI💁")
-
-    if "chat_history" not in st.session_state:
-        st.session_state.chat_history = []
-
-    with st.sidebar:
-#        st.logo("pic123.png")
-        st.image("pic123.png")
-        st.title("Menu:")
-        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
-        if st.button("Submit & Process"):
-            with st.spinner("Processing..."):
-                raw_text = get_pdf_text(pdf_docs)
-                text_chunks = get_text_chunks(raw_text)
-                get_vector_store(text_chunks)
-                st.success("Done")
-
-    # Display chat history
-    for idx, chat in enumerate(st.session_state.chat_history):
-        with st.chat_message(chat["role"]):
-            st.write(chat["content"])
-            if chat["role"] == "assistant":
-                st.download_button(
-                    label="Download",
-                    data=chat["content"],
-                    file_name=f"response_{idx}.txt",
-                    mime="text/plain",
-                    key=f"download_{idx}",
-                )
-
-    user_question = st.chat_input("Ask a Question from the PDF Files")
-
-    if user_question:
-        st.session_state.chat_history.append({"role": "user", "content": user_question})
-        st.chat_message("user").write(user_question)
-        user_input(user_question)
-        st.experimental_rerun()
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+from langchain_community.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+from langchain_openai import OpenAI, ChatOpenAI
+from langchain_openai import OpenAIEmbeddings
+
+load_dotenv()
+os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"]
+os.environ["LANGCHAIN_TRACING_V2"]="true"
+os.environ["LANGCHAIN_API_KEY"] = st.secrets["LANGCHAIN_API_KEY"]
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            try:
+                page_text = page.extract_text()
+                if page_text:
+                    text += page_text
+            except Exception as e:
+                print(f"Error reading page: {e}")
+    return text
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2500, chunk_overlap=750)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def get_vector_store(text_chunks):
+    vector_store = FAISS.from_texts(text_chunks, OpenAIEmbeddings())
+    vector_store.save_local("faiss_index")
+
+def get_conversational_chain():
+    prompt_template = """You are an assistant for teachers. Your objective is to provide
+    comprehensive and accurate responses based on the context provided. Make sure that 
+    you generate whole output.
+    context: {context}
+    question: {question}
+    """
+
+    model = ChatOpenAI(model="gpt-3.5-turbo")
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
+    return chain
+
+def user_input(user_question):
+    embeddings = OpenAIEmbeddings()
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(user_question)
+
+    chain = get_conversational_chain()
+    result = ""
+    with st.spinner("Processing..."):
+        response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
+        result = response["output_text"]
+    
+    st.session_state.chat_history.append({"role": "assistant", "content": result})
+
+def main():
+    st.set_page_config("Chat PDF")
+    st.header("Lesson Plan Maker With AI💁")
+
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
+
+    with st.sidebar:
+#        st.logo("pic123.png")
+        st.image("pic123.png")
+        st.title("Menu:")
+        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
+        if st.button("Submit & Process"):
+            with st.spinner("Processing..."):
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                st.success("Done")
+
+    # Display chat history
+    for idx, chat in enumerate(st.session_state.chat_history):
+        with st.chat_message(chat["role"]):
+            st.write(chat["content"])
+            if chat["role"] == "assistant":
+                st.download_button(
+                    label="Download",
+                    data=chat["content"],
+                    file_name=f"response_{idx}.txt",
+                    mime="text/plain",
+                    key=f"download_{idx}",
+                )
+
+    user_question = st.chat_input("Ask a Question from the PDF Files")
+
+    if user_question:
+        st.session_state.chat_history.append({"role": "user", "content": user_question})
+        st.chat_message("user").write(user_question)
+        user_input(user_question)
+        st.experimental_rerun()
+
+if __name__ == "__main__":
+    main()