Update app.py

app.py

@@ -1,68 +1,86 @@
-import streamlit as st
-import os
-import tempfile
-from langchain_community.vectorstores import FAISS
-from langchain_groq import ChatGroq
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_core.runnables import RunnablePassthrough
-from langchain.document_loaders import PyPDFLoader
-from langchain import hub
-
-# Set API key (replace with your actual key)
-os.environ["GROQ_API_KEY"] = "your_groq_api_key"
-
-# Streamlit UI
-st.title("📄 PDF Chatbot with RAG")
-st.write("Upload a PDF and ask questions!")
-
-# File uploader
-uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
-
-if uploaded_file:
-    # Save uploaded PDF temporarily
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
-        temp_file.write(uploaded_file.read())
-        temp_file_path = temp_file.name
-
-    # Load and process PDF
-    loader = PyPDFLoader(temp_file_path)
-    docs = loader.load()
-
-    # Initialize LLM and Embeddings
-    llm = ChatGroq(model="llama3-8b-8192")
-    model_name = "BAAI/bge-small-en"
-    hf_embeddings = HuggingFaceBgeEmbeddings(
-        model_name=model_name,
-        model_kwargs={'device': 'cpu'},
-        encode_kwargs={'normalize_embeddings': True}
-    )
-
-    # Split text
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    splits = text_splitter.split_documents(docs)
-
-    # Create FAISS vector store
-    vectorstore = FAISS.from_documents(documents=splits, embedding=hf_embeddings)
-    retriever = vectorstore.as_retriever()
-
-    # Load RAG prompt
-    prompt = hub.pull("rlm/rag-prompt")
-
-    def format_docs(docs):
-        return "\n\n".join(doc.page_content for doc in docs)
-
-    # RAG Chain
-    rag_chain = (
-        {"context": retriever | format_docs, "question": RunnablePassthrough()}
-        | prompt
-        | llm
-    )
-
-    # User Query
-    user_query = st.text_input("Ask a question from the PDF:")
-
-    if user_query:
-        response = rag_chain.invoke(user_query)
-        st.write("### 🤖 AI Response:")
-        st.write(response)
+import os
+import tempfile
+import gradio as gr
+from langchain_community.vectorstores import FAISS
+from langchain_groq import ChatGroq
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.runnables import RunnablePassthrough
+from langchain.document_loaders import PyPDFLoader
+from langchain import hub
+
+# Set API key (Replace with your actual key)
+os.environ["GROQ_API_KEY"] = "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"
+
+# Initialize LLM and Embeddings
+llm = ChatGroq(model="llama3-8b-8192")
+model_name = "BAAI/bge-small-en"
+hf_embeddings = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs={'device': 'cpu'},
+    encode_kwargs={'normalize_embeddings': True}
+)
+
+# Function to process PDF
+def process_pdf(file):
+    if file is None:
+        return "Please upload a PDF file."
+
+    # Save PDF temporarily
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
+        temp_file.write(file)
+        temp_file_path = temp_file.name
+
+    # Load and process PDF
+    loader = PyPDFLoader(temp_file_path)
+    docs = loader.load()
+
+    # Split text
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    splits = text_splitter.split_documents(docs)
+
+    # Create FAISS vector store
+    vectorstore = FAISS.from_documents(documents=splits, embedding=hf_embeddings)
+    retriever = vectorstore.as_retriever()
+
+    # Load RAG prompt
+    prompt = hub.pull("rlm/rag-prompt")
+
+    def format_docs(docs):
+        return "\n\n".join(doc.page_content for doc in docs)
+
+    # RAG Chain
+    global rag_chain
+    rag_chain = (
+        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+    )
+
+    return "PDF processed successfully! Now ask questions."
+
+# Function to answer queries
+def ask_question(query):
+    if "rag_chain" not in globals():
+        return "Please upload and process a PDF first."
+    
+    response = rag_chain.invoke(query)
+    return response
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 📄 PDF Chatbot with RAG")
+    gr.Markdown("Upload a PDF and ask questions!")
+    
+    pdf_input = gr.File(label="Upload PDF", type="binary")
+    process_button = gr.Button("Process PDF")
+    output_message = gr.Textbox(label="Status", interactive=False)
+    
+    query_input = gr.Textbox(label="Ask a Question")
+    submit_button = gr.Button("Submit")
+    response_output = gr.Textbox(label="AI Response")
+
+    process_button.click(process_pdf, inputs=pdf_input, outputs=output_message)
+    submit_button.click(ask_question, inputs=query_input, outputs=response_output)
+
+demo.launch()