Delete app.py

app.py

@@ -1,127 +0,0 @@
-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
-from serpapi import GoogleSearch
-
-
-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):
-
-    loader = PyPDFLoader(pdf_path)
-    documents = loader.load()
-    text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-    split_documents = text_splitter.split_documents(documents)
-
-    vectorstore = FAISS.from_documents(split_documents, embedding_model)
-    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
-    return retriever
-
-SERPAPI_KEY = "8a20e83850a3be0a0b4e3aed98bd3addbad56e82d52e639e1a692a02d021bca1"
-
-def scrape_google_search(query, num_results=3):
-    params = {
-        "q": query,
-        "hl": "fa",
-        "gl": "ir",
-        "num": num_results,
-        "api_key": SERPAPI_KEY,
-    }
-    search = GoogleSearch(params)
-    results = search.get_dict()
-
-    if "error" in results:
-        return f"Error: {results['error']}"
-
-    search_results = []
-    for result in results.get("organic_results", []):
-        title = result.get("title", "No Title")
-        link = result.get("link", "No Link")
-        search_results.append(f"{title}: {link}")
-    return "\n".join(search_results) if search_results else "No results found"
-
-def generate_response(query, retriever=None, use_web_search=False):
-
-    knowledge = ""
-
-    if retriever:
-        relevant_docs = retriever.get_relevant_documents(query)
-        knowledge += "\n".join([doc.page_content for doc in relevant_docs])
-
-    if use_web_search:
-        web_results = scrape_google_search(query)
-        knowledge += f"\n\nWeb Search Results:\n{web_results}"
-
-    chat_history = memory.load_memory_variables({}).get("chat_history", "")
-    context = (
-        f"This is a conversation with ParvizGPT, an AI model designed by Amir Mahdi Parviz from Kermanshah University of Technology (KUT), "
-        f"to help with tasks like answering questions in Persian, providing recommendations, and decision-making."
-    )
-    if knowledge:
-        context += f"\n\nRelevant Knowledge:\n{knowledge}"
-    if chat_history:
-        context += f"\n\nChat History:\n{chat_history}"
-
-    context += f"\n\nYou: {query}\nParvizGPT:"
-
-    chat_completion = client.chat.completions.create(
-        messages=[{"role": "user", "content": context}],
-        model="llama-3.3-70b-versatile",
-    )
-    response = chat_completion.choices[0].message.content.strip()
-
-    memory.save_context({"input": query}, {"output": response})
-    return response
-
-def gradio_interface(user_message, chat_box, pdf_file=None, enable_web_search=False):
-    global retriever
-    if pdf_file is not None:
-        try:
-            retriever = process_pdf_with_langchain(pdf_file.name)
-        except Exception as e:
-            return chat_box + [("Error", f"Error processing PDF: {e}")]
-
-    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,
-    )
-    enable_web_search = gr.Checkbox(label="🌐Enable Web Search", value=False)
-
-# 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)
-    pdf_file = gr.File(label="Upload PDF for Context (Optional)", type="filepath", interactive=True , scale=1)
-
-    submit_btn = gr.Button("Submit")
-    submit_btn.click(gradio_interface, inputs=[user_message, chat_box, pdf_file, enable_web_search], outputs=chat_box)
-    user_message.submit(gradio_interface, inputs=[user_message, chat_box, pdf_file, enable_web_search], outputs=chat_box)
-    clear_memory_btn.click(clear_memory, inputs=[], outputs=chat_box)
-
-interface.launch()