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
+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()