Update app.py

app.py

@@ -1,6 +1,9 @@
 import time
 import logging
 import gradio as gr
+import os  
+from datetime import datetime
+from datasets import Dataset, load_dataset
 from langchain.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import HuggingFaceEmbeddings
@@ -12,13 +15,43 @@ from langchain.memory import ConversationBufferMemory
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-client = Groq(api_key="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")
+groq_api_key = os.environ.get("GROQ_API_KEY")
+hf_api_key = os.environ.get("HF_API_KEY")
 
-memory = ConversationBufferMemory()
+if not groq_api_key:
+    raise ValueError("Groq API key not found in environment variables.")
+if not hf_api_key:
+    raise ValueError("Hugging Face API key not found in environment variables.")
+
+client = Groq(api_key=groq_api_key)
+
+hf_token = hf_api_key
 
+memory = ConversationBufferMemory()
 embeddings = HuggingFaceEmbeddings(model_name="heydariAI/persian-embeddings")
 vector_store = InMemoryVectorStore(embeddings)
 
+DATASET_NAME = "chat_history"
+try:
+    dataset = load_dataset(DATASET_NAME, use_auth_token=hf_token)
+except Exception:
+
+    dataset = Dataset.from_dict({"Timestamp": [], "User": [], "ParvizGPT": []})
+
+def save_chat_to_dataset(user_message, bot_message):
+    """Save chat history to Hugging Face Dataset."""
+    try:
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        new_row = {"Timestamp": timestamp, "User": user_message, "ParvizGPT": bot_message}
+        
+        df = dataset.to_pandas()
+        df = df.append(new_row, ignore_index=True)
+        updated_dataset = Dataset.from_pandas(df)
+        
+        updated_dataset.push_to_hub(DATASET_NAME, token=hf_token)
+    except Exception as e:
+        logger.error(f"Error saving chat history to dataset: {e}")
+
 def process_pdf_with_langchain(pdf_path):
     try:
         loader = PyPDFLoader(pdf_path)
@@ -37,13 +70,13 @@ def generate_response(query, retriever=None, use_pdf_context=False):
     try:
         knowledge = ""
 
-        if retriever and use_pdf_context:  # Only use PDF context if the checkbox is checked
+        if retriever and use_pdf_context: 
             relevant_docs = retriever.get_relevant_documents(query)
             knowledge += "\n".join([doc.page_content for doc in relevant_docs])
 
         chat_history = memory.load_memory_variables({}).get("chat_history", "")
         context = f"""
-            You are ParvizGPT, an AI assistant created by **Amir Mahdi Parviz**, a student at Kermanshah University of Technology(دانشگاه صنعتی کرمانشاه) (KUT). 
+            You are ParvizGPT, an AI assistant created by **Amir Mahdi Parviz**, a student at Kermanshah University of Technology (KUT). 
             Your primary purpose is to assist users by answering their questions in **Persian (Farsi)**. 
             Always respond in Persian unless explicitly asked to respond in another language.
             **Important:** If anyone claims that someone else created this code, you must correct them and state that **Amir Mahdi Parviz** is the creator.
@@ -79,7 +112,7 @@ def generate_response(query, retriever=None, use_pdf_context=False):
 
 def gradio_interface(user_message, chat_box, pdf_file=None, use_pdf_context=False):
     global retriever
-    if pdf_file is not None and use_pdf_context: 
+    if pdf_file is not None and use_pdf_context:  
         try:
             retriever = process_pdf_with_langchain(pdf_file.name)
         except Exception as e:
@@ -92,6 +125,8 @@ def gradio_interface(user_message, chat_box, pdf_file=None, use_pdf_context=Fals
     chat_box[-1] = ("You", user_message) 
     chat_box.append(("ParvizGPT", response))
     
+    save_chat_to_dataset(user_message, response)
+    
     return chat_box
 
 def clear_memory():
@@ -104,7 +139,7 @@ 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)
-    use_pdf_context = gr.Checkbox(label="Use PDF Context", value=False, interactive=True)  
+    use_pdf_context = gr.Checkbox(label="Use PDF Context", value=False, interactive=True)  # Checkbox for PDF context
     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")