Update app.py

app.py

@@ -1,126 +1,126 @@
-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
-
-
-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
-
-def scrape_google_search(query, num_results=3):
-
-    headers = {"User-Agent": "Mozilla/5.0"}
-    search_url = f"https://www.google.com/search?q={query}"
-    response = requests.get(search_url, headers=headers)
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    results = []
-    for g in soup.find_all('div', class_='tF2Cxc')[:num_results]:
-        title = g.find('h3').text
-        link = g.find('a')['href']
-        results.append(f"{title}: {link}")
-    return "\n".join(results)
-
-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, "
-        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" + "\n".join([f"User: {msg['content']}" for msg in 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, memory.load_memory_variables({}).get("chat_history", [])
-
-def gradio_interface(user_message, 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 f"Error processing PDF: {e}", []
-
-    response, chat_history = generate_response(user_message, retriever=retriever, use_web_search=enable_web_search)
-    chat_history_display = "\n".join([f"User: {msg['content']}" if msg['role'] == 'user' else f"ParvizGPT: {msg['content']}" for msg in chat_history])
-    return response, chat_history_display
-
-def clear_memory():
-    memory.clear()
-    return "Memory cleared!", ""
-
-retriever = None 
-
-with gr.Blocks() as interface:
-    gr.Markdown("## ParvizGPT")
-    with gr.Row():
-        user_message = gr.Textbox(label="Your Question", placeholder="Type your question here...", lines=1)
-    with gr.Row():
-        pdf_file = gr.File(label="Upload PDF for Context (Optional)", type="file")
-        enable_web_search = gr.Checkbox(label="Enable Web Search", value=False)
-
-    with gr.Row():
-        chat_history = gr.Textbox(label="Chat History", lines=10, interactive=False)
-
-    with gr.Row():
-        clear_memory_btn = gr.Button("Clear Memory")
-        submit_btn = gr.Button("Submit")
-
-
-    submit_btn.click(gradio_interface, inputs=[user_message, pdf_file, enable_web_search], outputs=[chat_history])
-    clear_memory_btn.click(clear_memory, inputs=[], outputs=[chat_history])
-
-    gr.HTML(
-        """
-        <script>
-        document.addEventListener("keydown", function(event) {
-            if (event.key === "Enter" && !event.shiftKey) {
-                event.preventDefault();
-                document.querySelector('button[title="Submit"]').click();
-            }
-        });
-        </script>
-        """
-    )
-
-interface.launch()
+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
+
+
+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
+
+def scrape_google_search(query, num_results=3):
+
+    headers = {"User-Agent": "Mozilla/5.0"}
+    search_url = f"https://www.google.com/search?q={query}"
+    response = requests.get(search_url, headers=headers)
+    soup = BeautifulSoup(response.text, "html.parser")
+
+    results = []
+    for g in soup.find_all('div', class_='tF2Cxc')[:num_results]:
+        title = g.find('h3').text
+        link = g.find('a')['href']
+        results.append(f"{title}: {link}")
+    return "\n".join(results)
+
+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, "
+        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" + "\n".join([f"User: {msg['content']}" for msg in 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, memory.load_memory_variables({}).get("chat_history", [])
+
+def gradio_interface(user_message, 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 f"Error processing PDF: {e}", []
+
+    response, chat_history = generate_response(user_message, retriever=retriever, use_web_search=enable_web_search)
+    chat_history_display = "\n".join([f"User: {msg['content']}" if msg['role'] == 'user' else f"ParvizGPT: {msg['content']}" for msg in chat_history])
+    return response, chat_history_display
+
+def clear_memory():
+    memory.clear()
+    return "Memory cleared!", ""
+
+retriever = None 
+
+with gr.Blocks() as interface:
+    gr.Markdown("## ParvizGPT")
+    with gr.Row():
+        user_message = gr.Textbox(label="Your Question", placeholder="Type your question here...", lines=1)
+    with gr.Row():
+        pdf_file = gr.File(label="Upload PDF for Context (Optional)", type="filepath")
+        enable_web_search = gr.Checkbox(label="Enable Web Search", value=False)
+
+    with gr.Row():
+        chat_history = gr.Textbox(label="Chat History", lines=10, interactive=False)
+
+    with gr.Row():
+        clear_memory_btn = gr.Button("Clear Memory")
+        submit_btn = gr.Button("Submit")
+
+
+    submit_btn.click(gradio_interface, inputs=[user_message, pdf_file, enable_web_search], outputs=[chat_history])
+    clear_memory_btn.click(clear_memory, inputs=[], outputs=[chat_history])
+
+    gr.HTML(
+        """
+        <script>
+        document.addEventListener("keydown", function(event) {
+            if (event.key === "Enter" && !event.shiftKey) {
+                event.preventDefault();
+                document.querySelector('button[title="Submit"]').click();
+            }
+        });
+        </script>
+        """
+    )
+
+interface.launch()