Update app.py

app.py

@@ -1,105 +1,115 @@
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-import os 
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-import google.generativeai as genai
-from langchain.vectorstores import FAISS
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from dotenv import load_dotenv
-
-
-load_dotenv()
-os.getenv("GOOGLE_API_KEY")
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-
-
-def get_pdf_text(pdf_docs):
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
-
-
-def get_text_chunks(text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-
-def get_vector_store(text_chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    vector_store.save_local("faiss_index")
-
-
-def get_conversational_chain():
-    prompt_template = """
-    Answer the question as detailed as possible from the provided context, make sure to provide all the details. If the answer is not in
-    the provided context, just say, "Answer is not available in the context." Don't provide the wrong answer.
-
-    Context:\n {context}?\n
-    Question: \n{question}\n
-
-    Answer:
-    """
-
-    model = ChatGoogleGenerativeAI(model="gemini-1.5-pro", temperature=0.3)
-    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 = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    
-    # Allow dangerous deserialization
-    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-    docs = new_db.similarity_search(user_question)
-
-    chain = get_conversational_chain()
-
-    response = chain(
-        {"input_documents": docs, "question": user_question},
-        return_only_outputs=True
-    )
-
-    print(response)
-    st.write("Reply: ", response.get("output_text", "No output generated"))
-
-
-def main():
-    st.set_page_config("Chat PDF")
-    st.header("Chat with PDF using Gemini💁")
-
-    user_question = st.text_input("Ask a Question from the PDF Files")
-    
-    # Add a Submit button for the user input
-    if st.button("Submit Question"):
-        if user_question:  # Ensure the input is not empty
-            user_input(user_question)
-        else:
-            st.warning("Please enter a question before submitting!")
-
-    with st.sidebar:
-        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")
-
-
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os 
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import google.generativeai as genai
+from langchain.vectorstores import FAISS
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+
+
+load_dotenv()
+os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def get_vector_store(text_chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+
+
+def get_conversational_chain():
+    prompt_template = """
+    Answer the question as detailed as possible from the provided context, make sure to provide all the details. If the answer is not in
+    the provided context, just say, "Answer is not available in the context." Don't provide the wrong answer.
+
+    Context:\n {context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+
+    model = ChatGoogleGenerativeAI(model="gemini-1.5-pro", temperature=0.3)
+    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 = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    
+    # Allow dangerous deserialization
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(user_question)
+
+    chain = get_conversational_chain()
+
+    response = chain(
+        {"input_documents": docs, "question": user_question},
+        return_only_outputs=True
+    )
+
+    print(response)
+    st.write("Reply: ", response.get("output_text", "No output generated"))
+
+
+def main():
+    st.set_page_config("Chat PDF")
+    st.header("Chat with PDF using Gemini💁")
+    
+    # Define a callback to clear input
+    def clear_input():
+        st.session_state["user_question"] = ""
+        st.balloons()
+
+    # Input box with key
+    st.text_input("Ask a Question from the PDF Files", key="user_question")
+
+    # Submit button
+    if st.button("Submit Question"):
+        if st.session_state.user_question:
+            user_input(st.session_state.user_question)
+        else:
+            st.warning("Please enter a question before submitting!")
+
+    # Clear button right below
+    st.button("Clear", on_click=clear_input)
+
+    # Sidebar for PDF upload
+    with st.sidebar:
+        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")
+
+
+
+if __name__ == "__main__":
+    main()