Update app.py

app.py

@@ -12,195 +12,31 @@ import logging
 from typing import List, Tuple
 from dataclasses import dataclass
 from datetime import datetime
-from transformers import AutoTokenizer, AutoModelForCausalLM ,pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from langchain_huggingface.llms import HuggingFacePipeline
 import spaces
 
+# [Previous imports and configurations remain the same]
 
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-@dataclass
-class Message:
-    role: str
-    content: str
-    timestamp: str
-
-class ChatHistory:
-    def __init__(self):
-        self.messages: List[Message] = []
-    
-    def add_message(self, role: str, content: str):
-        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        self.messages.append(Message(role=role, content=content, timestamp=timestamp))
-    
-    def get_formatted_history(self, max_messages: int = 5) -> str:
-        """Returns the most recent conversation history formatted as a string"""
-        recent_messages = self.messages[-max_messages:] if len(self.messages) > max_messages else self.messages
-        formatted_history = "\n".join([
-            f"{msg.role}: {msg.content}" for msg in recent_messages
-        ])
-        return formatted_history
-    
-    def clear(self):
-        self.messages = []
-
-# Load environment variables
-load_dotenv()
-
-# HuggingFace API Token
-HF_TOKEN = os.getenv("HF_TOKEN")
-if not HF_TOKEN:
-    logger.error("HF_TOKEN is not set in the environment variables.")
-    exit(1)
-
-# HuggingFace Embeddings
-embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en-v1.5")
-
-# Qdrant Client Setup
-try:
-    client = QdrantClient(
-        url=os.getenv("QDRANT_URL"),
-        api_key=os.getenv("QDRANT_API_KEY"),
-        prefer_grpc=True
-    )
-except Exception as e:
-    logger.error("Failed to connect to Qdrant. Ensure QDRANT_URL and QDRANT_API_KEY are correctly set.")
-    exit(1)
-
-# Define collection name
-collection_name = "mawared"
-
-# Try to create collection
-try:
-    client.create_collection(
-        collection_name=collection_name,
-        vectors_config=models.VectorParams(
-            size=768,  # GTE-large embedding size
-            distance=models.Distance.COSINE
-        )
-    )
-    logger.info(f"Created new collection: {collection_name}")
-except Exception as e:
-    if "already exists" in str(e):
-        logger.info(f"Collection {collection_name} already exists, continuing...")
-    else:
-        logger.error(f"Error creating collection: {e}")
-        exit(1)
-
-# Create Qdrant vector store
-db = Qdrant(
-    client=client,
-    collection_name=collection_name,
-    embeddings=embeddings,
-)
-
-# Create retriever
-retriever = db.as_retriever(
-    search_type="similarity",
-    search_kwargs={"k": 5}
-)
-
-
-# Load model directly
-
-
-
-# Set up the LLM
-llm = ChatOpenAI(
-    base_url="https://api-inference.huggingface.co/v1/",
-    temperature=0,
-    api_key=HF_TOKEN,
-    model="meta-llama/Llama-3.3-70B-Instruct",
-    max_tokens=None,
-    timeout=None
-    
-)
-
-# Create prompt template with chat history
-template = """
-You are an expert assistant specializing in the Mawared HR System. 
-Your task is to provide accurate and contextually relevant answers based on the provided context and chat history. 
-If you need more information, ask targeted clarifying questions.
-Ensure you provide detailed Numbered step by step to the user and be very accurate.
-Previous Conversation:
-{chat_history}
-Current Context:
-{context}
-Current Question:
-{question}
-Ask followup questions based on your provided asnwer to create a conversational flow, Only answer form the provided context and chat history , dont make up any information.
-answer only and only from the given context and knowledgebase.
-Answer:
-"""
-
-prompt = ChatPromptTemplate.from_template(template)
-
-# Create the RAG chain with chat history
-def create_rag_chain(chat_history: str):
-    chain = (
-        {
-            "context": retriever,
-            "question": RunnablePassthrough(),
-            "chat_history": lambda x: chat_history
-        }
-        | prompt
-        | llm
-        | StrOutputParser()
-    )
-    return chain
-
-# Initialize chat history
-chat_history = ChatHistory()
-
-# Gradio Function
-
-def ask_question_gradio(question, history):
-    try:
-        # Add user question to chat history
-        chat_history.add_message("user", question)
-        
-        # Get formatted history
-        formatted_history = chat_history.get_formatted_history()
-        
-        # Create chain with current chat history
-        rag_chain = create_rag_chain(formatted_history)
-        
-        # Generate response
-        response = ""
-        for chunk in rag_chain.stream(question):
-            response += chunk
-        
-        # Add assistant response to chat history
-        chat_history.add_message("assistant", response)
-        
-        # Update Gradio chat history
-        history.append({"role": "user", "content": question})
-        history.append({"role": "assistant", "content": response})
-        
-        return "", history
-    except Exception as e:
-        logger.error(f"Error during question processing: {e}")
-        return "", history + [{"role": "assistant", "content": "An error occurred. Please try again later."}]
-
-def clear_chat():
-    chat_history.clear()
-    return [], ""
-
-# Gradio Interface
+# Modified Gradio Interface
 with gr.Blocks(theme=gr.themes.Soft()) as iface:
-    gr.Image("Image.jpg" , width=1200 , height=300 ,show_label=False, show_download_button=False)
+    gr.Image("Image.jpg", width=1200, height=300, show_label=False, show_download_button=False)
     gr.Markdown("# Mawared HR Assistant")
     gr.Markdown("Ask questions about the Mawared HR system, and this assistant will provide answers based on the available context and conversation history.")
-
-           
     
-    chatbot = gr.Chatbot(
-        height=400,
-        show_label=False,
-        type="messages"  # Using the new messages format
-    )
+    # Create a state to store the latest assistant response
+    latest_response = gr.State("")
+    
+    with gr.Row():
+        chatbot = gr.Chatbot(
+            height=400,
+            show_label=False,
+            type="messages"
+        )
+    
+    with gr.Row():
+        # Add copy button next to the response
+        copy_button = gr.Button("📋 Copy Last Response", visible=True)
     
     with gr.Row():
         question_input = gr.Textbox(
@@ -210,6 +46,48 @@ with gr.Blocks(theme=gr.themes.Soft()) as iface:
         )
         clear_button = gr.Button("Clear Chat", scale=1)
     
+    def copy_last_response(history):
+        if history:
+            # Find the last assistant message
+            for message in reversed(history):
+                if message["role"] == "assistant":
+                    return message["content"]
+        return ""
+    
+    # Modified ask_question_gradio function to update the latest response
+    def ask_question_gradio(question, history):
+        try:
+            # Add user question to chat history
+            chat_history.add_message("user", question)
+            
+            # Get formatted history
+            formatted_history = chat_history.get_formatted_history()
+            
+            # Create chain with current chat history
+            rag_chain = create_rag_chain(formatted_history)
+            
+            # Generate response
+            response = ""
+            for chunk in rag_chain.stream(question):
+                response += chunk
+            
+            # Add assistant response to chat history
+            chat_history.add_message("assistant", response)
+            
+            # Update Gradio chat history
+            history.append({"role": "user", "content": question})
+            history.append({"role": "assistant", "content": response})
+            
+            return "", history
+        except Exception as e:
+            logger.error(f"Error during question processing: {e}")
+            return "", history + [{"role": "assistant", "content": "An error occurred. Please try again later."}]
+    
+    def clear_chat():
+        chat_history.clear()
+        return [], ""
+    
+    # Connect the components
     question_input.submit(
         ask_question_gradio,
         inputs=[question_input, chatbot],
@@ -220,6 +98,31 @@ with gr.Blocks(theme=gr.themes.Soft()) as iface:
         clear_chat,
         outputs=[chatbot, question_input]
     )
+    
+    # Add copy button functionality
+    copy_button.click(
+        copy_last_response,
+        inputs=[chatbot],
+        outputs=[],
+        _js="""
+        async (response) => {
+            await navigator.clipboard.writeText(response);
+            // Optional: Show a toast notification
+            const toast = document.createElement('div');
+            toast.textContent = 'Response copied to clipboard!';
+            toast.style.position = 'fixed';
+            toast.style.bottom = '20px';
+            toast.style.right = '20px';
+            toast.style.backgroundColor = '#4CAF50';
+            toast.style.color = 'white';
+            toast.style.padding = '15px';
+            toast.style.borderRadius = '5px';
+            toast.style.zIndex = '1000';
+            document.body.appendChild(toast);
+            setTimeout(() => toast.remove(), 2000);
+        }
+        """
+    )
 
 # Launch the Gradio App
 if __name__ == "__main__":