Update app.py

app.py

@@ -1,23 +1,21 @@
+import time
+import logging
 import gradio as gr
 from langchain.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings  # Updated for Persian embeddings
+from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
-from langchain.memory import ConversationBufferMemory
+from langchain_core.vectorstores import InMemoryVectorStore
 from groq import Groq
-import requests
-from bs4 import BeautifulSoup
-from serpapi import GoogleSearch
-import logging
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 client = Groq(api_key="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")
-
 embedding_model = HuggingFaceEmbeddings(model_name="heydariAI/persian-embeddings")
 
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+# Initialize in-memory vector store for chat history
+memory = InMemoryVectorStore()
 
 def process_pdf_with_langchain(pdf_path):
     try:
@@ -25,7 +23,7 @@ def process_pdf_with_langchain(pdf_path):
         documents = loader.load()
         text_splitter = RecursiveCharacterTextSplitter(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
@@ -33,49 +31,7 @@ def process_pdf_with_langchain(pdf_path):
         logger.error(f"Error processing PDF: {e}")
         raise
 
-SERPAPI_KEY = "8a20e83850a3be0a0b4e3aed98bd3addbad56e82d52e639e1a692a02d021bca1"
-
-def scrape_google_search(query, num_results=3):
-    try:
-        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"
-    except Exception as e:
-        logger.error(f"Error scraping Google search: {e}")
-        return f"Error: {e}"
-
-def scrape_webpage(url):
-    try:
-        headers = {
-            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
-        }
-        response = requests.get(url, headers=headers)
-        response.raise_for_status()
-
-        soup = BeautifulSoup(response.content, "html.parser")
-        text = soup.get_text(separator="\n")
-        return text.strip()
-    except Exception as e:
-        logger.error(f"Error scraping webpage {url}: {e}")
-        return f"Error: {e}"
-
-def generate_response(query, retriever=None, use_web_search=False, scrape_web=False):
+def generate_response(query, retriever=None):
     try:
         knowledge = ""
 
@@ -83,21 +39,9 @@ def generate_response(query, retriever=None, use_web_search=False, scrape_web=Fa
             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}"
-
-        if scrape_web:
-            urls = [word for word in query.split() if word.startswith("http://") or word.startswith("https://")]
-            for url in urls:
-                webpage_content = scrape_webpage(url)
-                knowledge += f"\n\nWebpage Content from {url}:\n{webpage_content}"
-
         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."
-        )
+        context = "This is a conversation with ParvizGPT, an AI model designed by Amir Mahdi Parviz from KUT."
+
         if knowledge:
             context += f"\n\nRelevant Knowledge:\n{knowledge}"
         if chat_history:
@@ -105,19 +49,32 @@ def generate_response(query, retriever=None, use_web_search=False, scrape_web=Fa
 
         context += f"\n\nYou: {query}\nParvizGPT:"
 
-        chat_completion = client.chat.completions.create(
-            messages=[{"role": "user", "content": context}],
-            model=  "deepseek-r1-distill-llama-70b"
-        )
-        response = chat_completion.choices[0].message.content.strip()
+        # ابتدا یک پیام موقت نمایش داده شود
+        response = "در حال پردازش..."
+
+        retries = 3
+        for attempt in range(retries):
+            try:
+                chat_completion = client.chat.completions.create(
+                    messages=[{"role": "user", "content": context}],
+                    model="deepseek-r1-distill-llama-70b"
+                )
+                response = chat_completion.choices[0].message.content.strip()
+                memory.save_context({"input": query}, {"output": response})
+                break
+            except Exception as e:
+                logger.error(f"Attempt {attempt + 1} failed: {e}")
+                time.sleep(2)
 
-        memory.save_context({"input": query}, {"output": response})
         return response
     except Exception as e:
         logger.error(f"Error generating response: {e}")
         return f"Error: {e}"
 
-def gradio_interface(user_message, chat_box, pdf_file=None, enable_web_search=False, scrape_web=False):
+
+
+        
+def gradio_interface(user_message, chat_box, pdf_file=None):
     global retriever
     if pdf_file is not None:
         try:
@@ -125,9 +82,13 @@ def gradio_interface(user_message, chat_box, pdf_file=None, enable_web_search=Fa
         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, scrape_web=scrape_web)
+    chat_box.append(("ParvizGPT", "در حال پردازش..."))
+    
+    response = generate_response(user_message, retriever=retriever)
+    
+    chat_box[-1] = ("ParvizGPT", response) 
     chat_box.append(("You", user_message))
-    chat_box.append(("ParvizGPT", response))
+    
     return chat_box
 
 def clear_memory():
@@ -139,24 +100,12 @@ retriever = None
 with gr.Blocks() as interface:
     gr.Markdown("## ParvizGPT")
     chat_box = gr.Chatbot(label="Chat History", value=[])
-
-    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)
-    scrape_web = gr.Checkbox(label="🌍Scrape Webpages", value=False)
-
+    user_message = gr.Textbox(label="Your Message", placeholder="Type your message here and press Enter...", lines=1, interactive=True)
     clear_memory_btn = gr.Button("Clear Memory", interactive=True)
-    pdf_file = gr.File(label="Upload PDF for Context (Optional)", type="filepath", interactive=True , scale=1)
-
+    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, scrape_web], outputs=chat_box)
-    user_message.submit(gradio_interface, inputs=[user_message, chat_box, pdf_file, enable_web_search, scrape_web], outputs=chat_box)
+    submit_btn.click(gradio_interface, inputs=[user_message, chat_box, pdf_file], outputs=chat_box)
+    user_message.submit(gradio_interface, inputs=[user_message, chat_box, pdf_file], outputs=chat_box)
     clear_memory_btn.click(clear_memory, inputs=[], outputs=chat_box)
 
 interface.launch()
-
-