Upload app.py

app.py

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+import gradio as gr
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import SentenceTransformerEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from groq import Groq
+import requests
+from bs4 import BeautifulSoup
+import time  # To simulate progress updates
+
+client = Groq(api_key="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")
+embedding_model = SentenceTransformerEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+def process_pdf_with_langchain(pdf_path, progress_callback):
+    # progress_callback("Initializing PDF processing... 0%")
+    time.sleep(0.5)
+    loader = PyPDFLoader(pdf_path)
+    # progress_callback("Loading PDF... 20%")
+    documents = loader.load()
+    time.sleep(0.5)
+    # progress_callback("Splitting documents... 50%")
+    text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    split_documents = text_splitter.split_documents(documents)
+    time.sleep(0.5)
+    # progress_callback("Creating vector store... 80%")
+    vectorstore = FAISS.from_documents(split_documents, embedding_model)
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+    progress_callback("Processing complete! 100%")
+    return retriever
+
+def upload_and_process(file, progress_display):
+    try:
+        global retriever
+        progress_updates = []
+        
+        retriever = process_pdf_with_langchain(file.name, lambda msg: progress_updates.append(msg))
+
+        return "\n".join(progress_updates), "File uploaded and processed successfully."
+    except Exception as e:
+        return "", f"Error processing file: {e}"
+
+def gradio_interface(user_message, chat_box, enable_web_search=False):
+    global retriever
+    response = generate_response(user_message, retriever=retriever, use_web_search=enable_web_search)
+    chat_box.append(("You", user_message))
+    chat_box.append(("ParvizGPT", response))
+    return chat_box
+
+def clear_memory():
+    memory.clear()
+    return []
+
+retriever = None
+with gr.Blocks() as interface:
+    gr.Markdown("## ParvizGPT")
+    with gr.Row():
+        chat_box = gr.Chatbot(label="Chat History", value=[])
+    with gr.Row():
+        user_message = gr.Textbox(
+            label="Your Message",
+            placeholder="Type your message here and press Enter...",
+            lines=1,
+            interactive=True,
+        )
+    with gr.Row():
+        clear_memory_btn = gr.Button("Clear Memory", interactive=True)
+        enable_web_search = gr.Checkbox(label="🌐Enable Web Search", value=False, interactive=True)
+    with gr.Row():
+        pdf_upload = gr.UploadButton(label="📄 Upload Your PDF", file_types=[".pdf"])
+        progress_display = gr.Textbox(label="Progress", placeholder="Progress updates will appear here", interactive=True)
+    with gr.Row():
+        submit_btn = gr.Button("Submit")
+    pdf_upload.upload(upload_and_process, inputs=[pdf_upload, progress_display], outputs=[progress_display])
+
+    submit_btn.click(gradio_interface, inputs=[user_message, chat_box, enable_web_search], outputs=chat_box)
+    user_message.submit(gradio_interface, inputs=[user_message, chat_box, enable_web_search], outputs=chat_box)
+    clear_memory_btn.click(clear_memory, inputs=[], outputs=chat_box)
+
+interface.launch()