Update app.py

app.py

@@ -1,162 +1,162 @@
-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.vectorstores import FAISS
-from langchain.memory import ConversationBufferMemory
-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="HooshvareLab/bert-fa-base-uncased")
-
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
-def process_pdf_with_langchain(pdf_path):
-    try:
-        loader = PyPDFLoader(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
-    except Exception as e:
-        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):
-    try:
-        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}"
-
-        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."
-        )
-        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=  "gemma2-9b-it" #"llama-3.3-70b-versatile",
-        )
-        response = chat_completion.choices[0].message.content.strip()
-
-        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):
-    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, scrape_web=scrape_web)
-    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")
-    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)
-
-    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)
-
-    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)
-    clear_memory_btn.click(clear_memory, inputs=[], outputs=chat_box)
-
-interface.launch()
-
-
+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.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+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)
+
+def process_pdf_with_langchain(pdf_path):
+    try:
+        loader = PyPDFLoader(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
+    except Exception as e:
+        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):
+    try:
+        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}"
+
+        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."
+        )
+        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=  "deepseek-r1-distill-llama-70b"
+        )
+        response = chat_completion.choices[0].message.content.strip()
+
+        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):
+    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, scrape_web=scrape_web)
+    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")
+    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)
+
+    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)
+
+    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)
+    clear_memory_btn.click(clear_memory, inputs=[], outputs=chat_box)
+
+interface.launch()
+
+