Update app.py

app.py

@@ -1,168 +1,168 @@
-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_community.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
-import traceback
-
-# Load environment variables
-load_dotenv()
-
-# Ensure the Google API key is loaded
-google_api_key = os.getenv("Google_API_Key")
-if not google_api_key:
-    raise ValueError("Google API key not found. Please check your .env file.")
-
-genai.configure(api_key=google_api_key)
-
-# Function to extract text from PDFs
-def get_pdf_text(pdf_docs):
-    text = ""
-    try:
-        for pdf in pdf_docs:
-            pdf_reader = PdfReader(pdf)
-            for page in pdf_reader.pages:
-                text += page.extract_text()
-    except Exception as e:
-        st.error(f"Error reading PDF files: {e}")
-    return text
-
-# Function to split text into manageable chunks
-def get_text_chunks(text):
-    try:
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
-        chunks = text_splitter.split_text(text)
-    except Exception as e:
-        st.error(f"Error splitting text: {e}")
-        return []
-    return chunks
-
-# Function to create an in-memory FAISS vector store
-def get_vector_store(text_chunks):
-    try:
-        embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-        vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-        return vector_store
-    except Exception as e:
-        st.error(f"Error creating vector store: {e}")
-        traceback.print_exc()
-        return None
-
-# Function to create a conversation chain with Google Generative AI
-def get_conversational_chain():
-    try:
-        prompt_template = """
-        Answer the question as detailed as possible from the provided context. If the answer is not in
-        the provided context, say, "Answer is not available in the context." Do not provide a wrong answer.
-
-        Context:
-        {context}
-
-        Question: 
-        {question}
-
-        Answer:
-        """
-
-        model = ChatGoogleGenerativeAI(model="gemini-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
-    except Exception as e:
-        st.error(f"Error creating conversation chain: {e}")
-        traceback.print_exc()
-        return None
-
-# Function to process user input and provide a response
-def user_input(user_question, vector_store):
-    try:
-        docs = vector_store.similarity_search(user_question)
-
-        chain = get_conversational_chain()
-        if chain:
-            response = chain(
-                {"input_documents": docs, "question": user_question},
-                return_only_outputs=True
-            )
-            st.markdown(f"<div style='font-size: 16px;'> 🤖 Response:: {response['output_text']}</div>", unsafe_allow_html=True)
-    except Exception as e:
-        st.error(f"Error processing user input: {e}")
-        traceback.print_exc()
-
-# Main function to handle Streamlit UI and actions
-def main():
-    # Set page title and icon
-    st.set_page_config(page_title="📚 Chat PDF with Gemini AI", layout="centered", page_icon="📖")
-    
-    # Add CSS for styling
-    st.markdown(
-        """
-        <style>
-        .main-header {
-            font-size: 36px;
-            font-weight: bold;
-            color: #0A74DA;
-        }
-        .instruction {
-            font-size: 18px;
-            margin-bottom: 20px;
-        }
-        
-        </style>
-        """,
-        unsafe_allow_html=True
-    )
-
-    # Add header
-    st.markdown("<h1 class='main-header'>Chat with Your PDF using Gemini AI 🤖</h1>", unsafe_allow_html=True)
-    st.markdown("<p class='instruction'>Upload your PDF, ask questions, and get detailed AI responses!</p>", unsafe_allow_html=True)
-
-    # Create a 2-column layout for better structure
-    col1, col2 = st.columns([12, 2])
-
-    with col1:
-        user_question = st.text_input("🔍 Ask a Question from the PDF Files", placeholder="Type your question here...")
-
-        # Add a "Submit" button to process the question
-        if st.button("Submit"):
-            if user_question:
-                st.write("### 🧠 Thinking...")
-                # Only allow submission if vector_store is available
-                if 'vector_store' in st.session_state:
-                    user_input(user_question, st.session_state.vector_store)
-                else:
-                    st.error("Please upload and process a PDF file first.")
-            else:
-                st.warning("Please enter a question before submitting.")
-
-    with col2:
-        with st.sidebar:
-            st.title("📂 PDF Upload & Processing")
-            st.write("1. Upload multiple PDFs.")
-            st.write("2. Ask questions based on the content.")
-            pdf_docs = st.file_uploader("Upload PDF Files", accept_multiple_files=True, type=["pdf"])
-
-            if st.button("Submit & Process PDFs"):
-                if pdf_docs:
-                    with st.spinner("📜 Extracting text and processing..."):
-                        raw_text = get_pdf_text(pdf_docs)
-                        if raw_text:
-                            text_chunks = get_text_chunks(raw_text)
-                            if text_chunks:
-                                vector_store = get_vector_store(text_chunks)
-                                if vector_store:
-                                    # Store vector store in session state to avoid re-processing
-                                    st.session_state.vector_store = vector_store
-                                    st.success("✅ Processing complete!")
-                else:
-                    st.warning("Please upload PDF files before processing.")
-
-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_community.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
+import traceback
+
+# Load environment variables
+load_dotenv()
+
+# Ensure the Google API key is loaded
+google_api_key = 'AIzaSyBPC1o6NSGFT2LumpdompngjOOzzUNwGqk'
+if not google_api_key:
+    raise ValueError("Google API key not found. Please check your .env file.")
+
+genai.configure(api_key=google_api_key)
+
+# Function to extract text from PDFs
+def get_pdf_text(pdf_docs):
+    text = ""
+    try:
+        for pdf in pdf_docs:
+            pdf_reader = PdfReader(pdf)
+            for page in pdf_reader.pages:
+                text += page.extract_text()
+    except Exception as e:
+        st.error(f"Error reading PDF files: {e}")
+    return text
+
+# Function to split text into manageable chunks
+def get_text_chunks(text):
+    try:
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+        chunks = text_splitter.split_text(text)
+    except Exception as e:
+        st.error(f"Error splitting text: {e}")
+        return []
+    return chunks
+
+# Function to create an in-memory FAISS vector store
+def get_vector_store(text_chunks):
+    try:
+        embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+        vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+        return vector_store
+    except Exception as e:
+        st.error(f"Error creating vector store: {e}")
+        traceback.print_exc()
+        return None
+
+# Function to create a conversation chain with Google Generative AI
+def get_conversational_chain():
+    try:
+        prompt_template = """
+        Answer the question as detailed as possible from the provided context. If the answer is not in
+        the provided context, say, "Answer is not available in the context." Do not provide a wrong answer.
+
+        Context:
+        {context}
+
+        Question: 
+        {question}
+
+        Answer:
+        """
+
+        model = ChatGoogleGenerativeAI(model="gemini-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
+    except Exception as e:
+        st.error(f"Error creating conversation chain: {e}")
+        traceback.print_exc()
+        return None
+
+# Function to process user input and provide a response
+def user_input(user_question, vector_store):
+    try:
+        docs = vector_store.similarity_search(user_question)
+
+        chain = get_conversational_chain()
+        if chain:
+            response = chain(
+                {"input_documents": docs, "question": user_question},
+                return_only_outputs=True
+            )
+            st.markdown(f"<div style='font-size: 16px;'> 🤖 Response:: {response['output_text']}</div>", unsafe_allow_html=True)
+    except Exception as e:
+        st.error(f"Error processing user input: {e}")
+        traceback.print_exc()
+
+# Main function to handle Streamlit UI and actions
+def main():
+    # Set page title and icon
+    st.set_page_config(page_title="📚 Chat PDF with Gemini AI", layout="centered", page_icon="📖")
+    
+    # Add CSS for styling
+    st.markdown(
+        """
+        <style>
+        .main-header {
+            font-size: 36px;
+            font-weight: bold;
+            color: #0A74DA;
+        }
+        .instruction {
+            font-size: 18px;
+            margin-bottom: 20px;
+        }
+        
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+    # Add header
+    st.markdown("<h1 class='main-header'>Chat with Your PDF using Gemini AI 🤖</h1>", unsafe_allow_html=True)
+    st.markdown("<p class='instruction'>Upload your PDF, ask questions, and get detailed AI responses!</p>", unsafe_allow_html=True)
+
+    # Create a 2-column layout for better structure
+    col1, col2 = st.columns([12, 2])
+
+    with col1:
+        user_question = st.text_input("🔍 Ask a Question from the PDF Files", placeholder="Type your question here...")
+
+        # Add a "Submit" button to process the question
+        if st.button("Submit"):
+            if user_question:
+                st.write("### 🧠 Thinking...")
+                # Only allow submission if vector_store is available
+                if 'vector_store' in st.session_state:
+                    user_input(user_question, st.session_state.vector_store)
+                else:
+                    st.error("Please upload and process a PDF file first.")
+            else:
+                st.warning("Please enter a question before submitting.")
+
+    with col2:
+        with st.sidebar:
+            st.title("📂 PDF Upload & Processing")
+            st.write("1. Upload multiple PDFs.")
+            st.write("2. Ask questions based on the content.")
+            pdf_docs = st.file_uploader("Upload PDF Files", accept_multiple_files=True, type=["pdf"])
+
+            if st.button("Submit & Process PDFs"):
+                if pdf_docs:
+                    with st.spinner("📜 Extracting text and processing..."):
+                        raw_text = get_pdf_text(pdf_docs)
+                        if raw_text:
+                            text_chunks = get_text_chunks(raw_text)
+                            if text_chunks:
+                                vector_store = get_vector_store(text_chunks)
+                                if vector_store:
+                                    # Store vector store in session state to avoid re-processing
+                                    st.session_state.vector_store = vector_store
+                                    st.success("✅ Processing complete!")
+                else:
+                    st.warning("Please upload PDF files before processing.")
+
+if __name__ == "__main__":
+    main()