Create app.py

app.py

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+import gradio as gr
+import PyPDF2
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+from langchain_groq import ChatGroq
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.document_loaders import WebBaseLoader
+import os
+
+# Function to process text and create ConversationalRetrievalChain
+def process_text_and_create_chain(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    texts = text_splitter.split_text(text)
+    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
+    
+    model_name = "BAAI/bge-small-en"
+    model_kwargs = {"device": "cpu"}
+    encode_kwargs = {"normalize_embeddings": True}
+    hf = HuggingFaceBgeEmbeddings(
+        model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
+    )
+    
+    db = Chroma.from_texts(texts, hf, metadatas=metadatas)
+    
+    message_history = ChatMessageHistory()
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key="answer",
+        chat_memory=message_history,
+        return_messages=True,
+    )
+    
+    llm_groq = ChatGroq(
+        groq_api_key="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",
+        model_name='mixtral-8x7b-32768'
+    )
+    
+    chain = ConversationalRetrievalChain.from_llm(
+        llm=llm_groq,
+        chain_type="stuff",
+        retriever=db.as_retriever(),
+        memory=memory,
+        return_source_documents=True,
+    )
+    
+    return chain
+
+# Initialize global variables
+global_chain = None
+
+# Function to handle PDF upload
+def handle_pdf_upload(file):
+    if file is None:
+        return "No file uploaded. Please upload a PDF file.", gr.update(visible=False), gr.update(visible=True)
+    
+    if not file.name.lower().endswith('.pdf'):
+        return "Error: Please upload a PDF file.", gr.update(visible=False), gr.update(visible=True)
+    
+    try:
+        print(f"Processing file: {file.name}")
+        pdf_reader = PyPDF2.PdfReader(file.name)
+        pdf_text = ""
+        for page in pdf_reader.pages:
+            pdf_text += page.extract_text()
+        
+        global global_chain
+        global_chain = process_text_and_create_chain(pdf_text)
+        return "PDF processed successfully.", gr.update(visible=True), gr.update(visible=False)
+    except Exception as e:
+        print(f"Error processing PDF: {str(e)}")
+        return f"Error processing PDF: {str(e)}", gr.update(visible=False), gr.update(visible=True)
+
+# Function to handle link input
+def handle_link_input(link):
+    try:
+        loader = WebBaseLoader(link)
+        data = loader.load()
+        doc = "\n".join([doc.page_content for doc in data])
+        
+        global global_chain
+        global_chain = process_text_and_create_chain(doc)
+        return "Link processed successfully.", gr.update(visible=True), gr.update(visible=False)
+    except Exception as e:
+        print(f"Error processing link: {str(e)}")
+        return f"Error processing link: {str(e)}", gr.update(visible=False), gr.update(visible=True)
+
+# Function to handle user query
+def handle_query(query, chatbot):
+    if global_chain is None:
+        return chatbot + [("Bot", "Please provide input first.")]
+    try:
+        result = global_chain({"question": query})
+        return chatbot + [("You", query), ("System", result['answer'])]
+    except Exception as e:
+        print(f"Error processing query: {str(e)}")
+        return chatbot + [("Bot", f"Error: {str(e)}")]
+
+# Function to toggle input method
+def toggle_input_method(input_method):
+    if input_method == "Upload PDF":
+        return gr.update(visible=True), gr.update(visible=False)
+    elif input_method == "Paste Link":
+        return gr.update(visible=False), gr.update(visible=True)
+    else:
+        return gr.update(visible=False), gr.update(visible=False)
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Chat-With-Context")
+    
+    with gr.Row():
+        input_method = gr.Radio(["Upload PDF", "Paste Link"], label="Choose Input Method", interactive=True)
+    
+    with gr.Row(visible=False) as upload_section:
+        pdf_input = gr.File(label="Upload PDF")
+        upload_button = gr.Button("Process PDF")
+
+    with gr.Row(visible=False) as text_input_section:
+        text_input = gr.Textbox(label="Paste Link")
+        submit_text_button = gr.Button("Process Link")
+
+    input_status = gr.Textbox(label="Status", interactive=False)
+
+    with gr.Row(visible=False) as chat_section:
+        chatbot = gr.Chatbot(label="Chat")
+        query_input = gr.Textbox(label="Write Your Question", placeholder="Message Chat-With-Context")
+        send_button = gr.Button("Send")
+
+    input_method.change(toggle_input_method, inputs=input_method, outputs=[upload_section, text_input_section])
+    upload_button.click(fn=handle_pdf_upload, inputs=pdf_input, outputs=[input_status, chat_section, upload_section])
+    submit_text_button.click(fn=handle_link_input, inputs=text_input, outputs=[input_status, chat_section, text_input_section])
+    send_button.click(fn=handle_query, inputs=[query_input, chatbot], outputs=chatbot)
+
+
+
+demo.launch(share=True)