Update app.py

app.py

@@ -1,253 +1,7 @@
-import os
-import gradio as gr
-import google.generativeai as genai
-from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
-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
+from diffusers import DiffusionPipeline
 
-# Get API key from Hugging Face Spaces secrets
-google_api_key = os.environ.get("GOOGLE_API_KEY")
+pipe = DiffusionPipeline.from_pretrained("Yw22/BlobCtrl")
 
-if not google_api_key:
-    raise ValueError("GOOGLE_API_KEY not found in environment variables. Please add it to Hugging Face Space secrets.")
+prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
+image = pipe(prompt).images[0]
 
-# Configure Google Generative AI
-genai.configure(api_key=google_api_key)
-
-# 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
-
-# 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")
-
-        # Generate embeddings
-        embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-
-        # 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})
-    
-    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:
-        return ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
-    except Exception as e:
-        print(f"Error initializing LLM: {str(e)}")
-        return None
-
-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:"""
-
-        response = llm.invoke(prompt)
-        return response.content
-    except Exception as e:
-        return f"Error in RAG processing: {str(e)}"
-
-# 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 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 get response
-        response = chain.run(date=current_date, time=current_time, query=query)
-        return response
-    
-    except Exception as e:
-        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!"
-
-# 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()
-    
-    # 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;
-    }
-    """
-    
-    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>")
-
-        # 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(share=True)
-
-if __name__ == "__main__":
-    main()