Update app.py

app.py

@@ -1,215 +1,253 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[144]:
-
-
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
-
 import os
-
+import gradio as gr
 import google.generativeai as genai
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
-from langchain.vectorstores import FAISS
-import gradio as gr
-
-
-os.environ["MY_SECRET_KEY"] = "AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo"
-
-
-# In[145]:
-
-
-#pip install pypdf
-#!pip install faiss-cpu
-
-
-# In[146]:
-
-
-google_api_key = os.environ["MY_SECRET_KEY"]
-
-# Check if the API key was found
-if google_api_key:
-    # Set the environment variable if the API key was found
-    os.environ["GOOGLE_API_KEY"] = google_api_key
-
-    llm = ChatGoogleGenerativeAI(
-        model="gemini-pro",  # Specify the model name
-        google_api_key=os.environ["GOOGLE_API_KEY"]
-    )
-else:
-    print("Error: GOOGLE_API_KEY not found in Colab secrets. Please store your API key.")
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from datetime import datetime
+import pytz
+import time
 
+# Get API key from Hugging Face Spaces secrets
+google_api_key = os.environ.get("GOOGLE_API_KEY")
 
+if not google_api_key:
+    raise ValueError("GOOGLE_API_KEY not found in environment variables. Please add it to Hugging Face Space secrets.")
 
+# Configure Google Generative AI
 genai.configure(api_key=google_api_key)
-model = genai.GenerativeModel("gemini-pro")
-
-
-# In[147]:
-
-
-work_dir=os.getcwd()
-
-
-# In[148]:
-
-
-# Verify file existence
-assert "RAG.pdf" in os.listdir(work_dir), "RAG.pdf not found in the specified directory!"
-print(f"Current Working Directory: {os.getcwd()}")
-
-
-# In[149]:
-
-
-# Load PDF and split text
-pdf_path = "RAG.pdf"  # Ensure this file is uploaded to Colab
-loader = PyPDFLoader(pdf_path)
-documents = loader.load()
-
-# Split text into chunks
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10)
-text_chunks = text_splitter.split_documents(documents)
-
-
-# In[150]:
-
-
-# Generate embeddings
-embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-
-# Store embeddings in FAISS index
-vectorstore = FAISS.from_documents(text_chunks, embeddings)
-retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
-
-
-# In[151]:
-
-
-# Set up Gemini model
-llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001", temperature=0)
-#llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0)
 
+# Function to get current date and time
+def get_current_datetime():
+    # Using UTC as default, but you can change to any timezone
+    utc_now = datetime.now(pytz.UTC)
+    
+    # Convert to IST (Indian Standard Time) - modify as needed
+    ist_timezone = pytz.timezone('Asia/Kolkata')
+    ist_now = utc_now.astimezone(ist_timezone)
+    
+    # Format the datetime
+    formatted_date = ist_now.strftime("%B %d, %Y")
+    formatted_time = ist_now.strftime("%I:%M:%S %p")
+    
+    return formatted_date, formatted_time
 
-# In[152]:
+# Load PDF and create vector store
+def initialize_retriever():
+    try:
+        # Get current directory
+        current_dir = os.getcwd()
+        print(f"Current working directory: {current_dir}")
+        
+        # List files in current directory for debugging
+        print(f"Files in directory: {os.listdir(current_dir)}")
+        
+        # Use absolute path for the PDF
+        pdf_path = os.path.join(current_dir, "Team1.pdf")
+        print(f"Attempting to load PDF from: {pdf_path}")
+        
+        # Check if file exists
+        if not os.path.exists(pdf_path):
+            raise FileNotFoundError(f"The file {pdf_path} does not exist")
+            
+        # Load PDF
+        loader = PyPDFLoader(pdf_path)
+        documents = loader.load()
+        
+        print(f"Successfully loaded {len(documents)} pages from the PDF")
 
+        # Split text into chunks
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10)
+        text_chunks = text_splitter.split_documents(documents)
+        print(f"Split into {len(text_chunks)} text chunks")
 
-import gradio as gr
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
+        # Generate embeddings
+        embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
 
-def rag_query(query):
-    # Retrieve relevant documents
-    docs = retriever.get_relevant_documents(query)
+        # Store embeddings in FAISS index
+        vectorstore = FAISS.from_documents(text_chunks, embeddings)
+        print("Successfully created vector store")
+        return vectorstore.as_retriever(search_kwargs={"k": 4})
     
-    # Otherwise, use RAG
-    context = "\n".join([doc.page_content for doc in docs])
-    prompt = f"Context:\n{context}\n\nQuestion: {query}\nAnswer directly and concisely:"
+    except Exception as e:
+        print(f"Error in initialize_retriever: {str(e)}")
+        # Return a dummy retriever for graceful failure
+        class DummyRetriever:
+            def get_relevant_documents(self, query):
+                return []
+        
+        print("Returning dummy retriever due to error")
+        return DummyRetriever()
 
+# Initialize LLM
+def get_llm():
     try:
-        response = llm.invoke(prompt)
+        return ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
     except Exception as e:
-        response = f"Error in RAG processing: {str(e)}"
-
-    return response.content
-
-
-
+        print(f"Error initializing LLM: {str(e)}")
+        return None
 
-# In[153]:
+llm = get_llm()
 
+# RAG query function
+def rag_query(query, retriever):
+    if retriever is None:
+        return "Error: Could not initialize document retriever. Please check if Team1.pdf exists."
+    
+    # Get current date and time for context
+    current_date, current_time = get_current_datetime()
+    
+    try:
+        # Retrieve relevant documents
+        docs = retriever.get_relevant_documents(query)
+        
+        if not docs:
+            return "No relevant information found in the document. Try a general query instead."
+        
+        # Create context from retrieved documents
+        context = "\n".join([doc.page_content for doc in docs])
+        prompt = f"""Context:\n{context}
+Current Date: {current_date}
+Current Time: {current_time}
+Question: {query}
+Answer directly and concisely, using the current date and time information if relevant:"""
 
-import gradio as gr
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
-from langchain_google_genai import ChatGoogleGenerativeAI
-
-# Initialize LLM once (avoid repeated initialization)
-llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
+        response = llm.invoke(prompt)
+        return response.content
+    except Exception as e:
+        return f"Error in RAG processing: {str(e)}"
 
-# Define the general query function
+# General query function
 def general_query(query):
+    if llm is None:
+        return "Error: Could not initialize language model. Please check your API key."
+    
+    # Get current date and time for context
+    current_date, current_time = get_current_datetime()
+    
     try:
-        # Define the prompt correctly
-        prompt = PromptTemplate.from_template("Answer the following query: {query}")
+        # Define the prompt with date and time context
+        prompt_template = """Current Date: {date}
+Current Time: {time}
+Answer the following query, using the current date and time information if relevant: {query}"""
+        
+        prompt = PromptTemplate.from_template(prompt_template)
         
         # Create an LLM Chain
         chain = LLMChain(llm=llm, prompt=prompt)
         
-        # Run chatbot and return response
-        response = chain.run(query=query)
-        
-        return response  # Return response directly (not response.content)
+        # Run chatbot and get response
+        response = chain.run(date=current_date, time=current_time, query=query)
+        return response
     
     except Exception as e:
-        return f"Error: {str(e)}"
-
-
-
-# In[154]:
-
-
-import gradio as gr
-
-
-# Function to call the selected query method
-def query_router(query, method):
-    if method == "Team Query":  # Ensure exact match with dropdown options
-        return rag_query(query)
+        return f"Error in general query: {str(e)}"
+
+# Function to handle the case when no PDF is found
+def file_not_found_message():
+    return ("The Team1.pdf file could not be found. Team Query mode will not work properly. "
+            "Please ensure the PDF is correctly uploaded to the Hugging Face Space.")
+
+# Query router function
+def query_router(query, method, retriever):
+    if method == "Team Query":
+        if isinstance(retriever, type) or retriever is None:
+            return file_not_found_message()
+        return rag_query(query, retriever)
     elif method == "General Query":
         return general_query(query)
     return "Invalid selection!"
 
-# Define local image paths
-logo_path = "Equinix-LOGO.jpeg"  # Ensure this file exists
-
-# Custom CSS for background styling
-custom_css = """
-.gradio-container {
-    background-color: #f0f0f0;
-    text-align: center;
-}
-#logo img {
-    display: block;
-    margin: 0 auto;
-    max-width: 200px; /* Adjust size */
-}
-"""
-
-# Create Gradio UI
-with gr.Blocks(css=custom_css) as ui:
-    gr.Image(logo_path, elem_id="logo", show_label=False, height=100, width=200)  # Display Logo
+# Function to update the clock
+def update_datetime():
+    date, time = get_current_datetime()
+    return date, time
+
+# Main function to create and launch the Gradio interface
+def main():
+    # Initialize retriever
+    print("Initializing retriever...")
+    retriever = initialize_retriever()
     
-    # Title & Description
-    gr.Markdown("<h1 style='text-align: center; color: black;'>Equinix Chatbot for Automation Team</h1>")
-    gr.Markdown("<p style='text-align: center; color: black;'>Ask me anything!</p>")
-
-    # Input & Dropdown Section
-    with gr.Row():
-        query_input = gr.Textbox(label="Enter your query")
-        query_method = gr.Dropdown(["Team Query", "General Query"], label="Select Query Type")
+    # Custom CSS for styling
+    custom_css = """
+    .gradio-container {
+        background-color: #f0f0f0;
+        text-align: center;
+    }
+    #logo img {
+        display: block;
+        margin: 0 auto;
+        max-width: 200px;
+    }
+    .datetime-display {
+        text-align: center;
+        margin-bottom: 20px;
+        font-size: 18px;
+        font-weight: bold;
+    }
+    """
     
-    # Button for submitting query
-    submit_button = gr.Button("Submit")
-
-    # Output Textbox
-    output_box = gr.Textbox(label="Response", interactive=False)
-
-    # Button Click Event
-    submit_button.click(query_router, inputs=[query_input, query_method], outputs=output_box)
-
-# Launch UI
-ui.launch(share=True)
-
-
-# In[168]:
-
+    logo_path = "equinix-sign.jpg"
+    logo_exists = os.path.exists(logo_path)
+    
+    # Create Gradio UI
+    with gr.Blocks(css=custom_css) as ui:
+        if logo_exists:
+            gr.Image(logo_path, elem_id="logo", show_label=False, height=100, width=200)
+        else:
+            gr.Markdown("<h2 style='text-align: center;'>Equinix</h2>")
+            print(f"Warning: Logo file {logo_path} not found")
+        
+        # Title & Description
+        gr.Markdown("<h1 style='text-align: center; color: black;'>Equinix Chatbot for Automation Team</h1>")
+        
+        # Date and Time Display
+        with gr.Row(elem_classes="datetime-display"):
+            date_display = gr.Textbox(label="Date", interactive=False)
+            time_display = gr.Textbox(label="Time", interactive=False)
+        
+        # Update date and time using Gradio's interval functionality
+        date_val, time_val = get_current_datetime()
+        date_display.value = date_val
+        time_display.value = time_val
+        
+        # Add refresh button for time
+        refresh_btn = gr.Button("Update Date & Time")
+        refresh_btn.click(fn=update_datetime, inputs=[], outputs=[date_display, time_display])
+        
+        gr.Markdown("<p style='text-align: center; color: black;'>Ask me anything!</p>")
 
-get_ipython().system('jupyter nbconvert --to script GenAI_1.ipynb')
+        # Input & Dropdown Section
+        with gr.Row():
+            query_input = gr.Textbox(label="Enter your query")
+            query_method = gr.Dropdown(["Team Query", "General Query"], label="Select Query Type", value="Team Query")
+        
+        # Button for submitting query
+        submit_button = gr.Button("Submit")
+
+        # Output Textbox
+        output_box = gr.Textbox(label="Response", interactive=False)
+
+        # Button Click Events
+        submit_button.click(
+            lambda query, method: query_router(query, method, retriever), 
+            inputs=[query_input, query_method], 
+            outputs=output_box
+        )
+        
+        # This callback will update the date and time whenever the user submits a query
+        submit_button.click(
+            fn=update_datetime,
+            inputs=[],
+            outputs=[date_display, time_display]
+        )
+    
+    # Launch UI
+    ui.launch()
 
+if __name__ == "__main__":
+    main()