app.py

import gradio as gr
import groq
import os
import tempfile
import uuid
from dotenv import load_dotenv
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
import fitz  # PyMuPDF
import base64
from PIL import Image
import io

# Load environment variables
load_dotenv()
client = groq.Client(api_key=os.getenv("GROQ_LEGAL_API_KEY"))
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

# Directory to store FAISS indexes
FAISS_INDEX_DIR = "faiss_indexes_tech"
if not os.path.exists(FAISS_INDEX_DIR):
    os.makedirs(FAISS_INDEX_DIR)

# Dictionary to store user-specific vectorstores
user_vectorstores = {}

# Custom CSS for Tech theme
custom_css = """
:root {
    --primary-color: #008080;  /* Teal */
    --secondary-color: #006666;  /* Dark Teal */
    --light-background: #E0FFFF;  /* Light Cyan */
    --dark-text: #333333;
    --white: #FFFFFF;
    --border-color: #E5E7EB;
}
body { background-color: var(--light-background); font-family: 'Inter', sans-serif; }
.container { max-width: 1200px !important; margin: 0 auto !important; padding: 10px; }
.header { background-color: var(--white); border-bottom: 2px solid var(--border-color); padding: 15px 0; margin-bottom: 20px; border-radius: 12px 12px 0 0; box-shadow: 0 2px 4px rgba(0,0,0,0.05); }
.header-title { color: var(--secondary-color); font-size: 1.8rem; font-weight: 700; text-align: center; }
.header-subtitle { color: var(--dark-text); font-size: 1rem; text-align: center; margin-top: 5px; }
.chat-container { border-radius: 12px !important; box-shadow: 0 4px 6px rgba(0,0,0,0.1) !important; background-color: var(--white) !important; border: 1px solid var(--border-color) !important; min-height: 500px; }
.message-user { background-color: var(--primary-color) !important; color: var(--white) !important; border-radius: 18px 18px 4px 18px !important; padding: 12px 16px !important; margin-left: auto !important; max-width: 80% !important; }
.message-bot { background-color: #F0F0F0 !important; color: var(--dark-text) !important; border-radius: 18px 18px 18px 4px !important; padding: 12px 16px !important; margin-right: auto !important; max-width: 80% !important; }
.input-area { background-color: var(--white) !important; border-top: 1px solid var(--border-color) !important; padding: 12px !important; border-radius: 0 0 12px 12px !important; }
.input-box { border: 1px solid var(--border-color) !important; border-radius: 24px !important; padding: 12px 16px !important; box-shadow: 0 2px 4px rgba(0,0,0,0.05) !important; }
.send-btn { background-color: var(--secondary-color) !important; border-radius: 24px !important; color: var(--white) !important; padding: 10px 20px !important; font-weight: 500 !important; }
.clear-btn { background-color: #F0F0F0 !important; border: 1px solid var(--border-color) !important; border-radius: 24px !important; color: var(--dark-text) !important; padding: 8px 16px !important; font-weight: 500 !important; }
.pdf-viewer-container { border-radius: 12px !important; box-shadow: 0 4px 6px rgba(0,0,0,0.1) !important; background-color: var(--white) !important; border: 1px solid var(--border-color) !important; padding: 20px; }
.pdf-viewer-image { max-width: 100%; height: auto; border: 1px solid var(--border-color); border-radius: 12px; box-shadow: 0 2px 4px rgba(0,0,0,0.05); }
.stats-box { background-color: #E0F0F0; padding: 10px; border-radius: 8px; margin-top: 10px; }
"""

# Function to process PDF files (unchanged)
def process_pdf(pdf_file):
    if pdf_file is None:
        return None, "No file uploaded", {"page_images": [], "total_pages": 0, "total_words": 0}
    try:
        session_id = str(uuid.uuid4())
        with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as temp_file:
            temp_file.write(pdf_file)
            pdf_path = temp_file.name
        
        doc = fitz.open(pdf_path)
        texts = [page.get_text() for page in doc]
        page_images = []
        for page in doc:
            pix = page.get_pixmap()
            img_bytes = pix.tobytes("png")
            img_base64 = base64.b64encode(img_bytes).decode("utf-8")
            page_images.append(img_base64)
        total_pages = len(doc)
        total_words = sum(len(text.split()) for text in texts)
        doc.close()

        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        chunks = text_splitter.create_documents(texts)
        vectorstore = FAISS.from_documents(chunks, embeddings)
        index_path = os.path.join(FAISS_INDEX_DIR, session_id)
        vectorstore.save_local(index_path)
        user_vectorstores[session_id] = vectorstore

        os.unlink(pdf_path)
        pdf_state = {"page_images": page_images, "total_pages": total_pages, "total_words": total_words}
        return session_id, f"✅ Successfully processed {len(chunks)} text chunks from your PDF", pdf_state
    except Exception as e:
        if "pdf_path" in locals() and os.path.exists(pdf_path):
            os.unlink(pdf_path)
        return None, f"Error processing PDF: {str(e)}", {"page_images": [], "total_pages": 0, "total_words": 0}

# Function to generate chatbot responses with Tech theme
def generate_response(message, session_id, model_name, history):
    if not message:
        return history
    try:
        context = ""
        if session_id and session_id in user_vectorstores:
            vectorstore = user_vectorstores[session_id]
            docs = vectorstore.similarity_search(message, k=3)
            if docs:
                context = "\n\nRelevant information from uploaded PDF:\n" + "\n".join(f"- {doc.page_content}" for doc in docs)
        system_prompt = "You are a technical assistant specializing in analyzing tech manuals, whitepapers, and documentation."
        if context:
            system_prompt += " Use the following context to answer the question if relevant: " + context
        completion = client.chat.completions.create(
            model=model_name,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": message}
            ],
            temperature=0.7,
            max_tokens=1024
        )
        response = completion.choices[0].message.content
        history.append((message, response))
        return history
    except Exception as e:
        history.append((message, f"Error generating response: {str(e)}"))
        return history

# Functions to update PDF viewer (unchanged)
def update_pdf_viewer(pdf_state):
    if not pdf_state["total_pages"]:
        return 0, None, "No PDF uploaded yet"
    try:
        img_data = base64.b64decode(pdf_state["page_images"][0])
        img = Image.open(io.BytesIO(img_data))
        return pdf_state["total_pages"], img, f"**Total Pages:** {pdf_state['total_pages']}\n**Total Words:** {pdf_state['total_words']}"
    except Exception as e:
        print(f"Error decoding image: {e}")
        return 0, None, "Error displaying PDF"

def update_image(page_num, pdf_state):
    if not pdf_state["total_pages"] or page_num < 1 or page_num > pdf_state["total_pages"]:
        return None
    try:
        img_data = base64.b64decode(pdf_state["page_images"][page_num - 1])
        img = Image.open(io.BytesIO(img_data))
        return img
    except Exception as e:
        print(f"Error decoding image: {e}")
        return None

# Gradio interface
with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
    current_session_id = gr.State(None)
    pdf_state = gr.State({"page_images": [], "total_pages": 0, "total_words": 0})
    gr.HTML("""
    <div class="header">
        <div class="header-title">Tech-Vision</div>
        <div class="header-subtitle">Analyze technical documents with Groq's LLM API.</div>
    </div>
    """)
    with gr.Row(elem_classes="container"):
        with gr.Column(scale=1, min_width=300):
            pdf_file = gr.File(label="Upload PDF Document", file_types=[".pdf"], type="binary")
            upload_button = gr.Button("Process PDF", variant="primary")
            pdf_status = gr.Markdown("No PDF uploaded yet")
            model_dropdown = gr.Dropdown(
                choices=["llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma-7b-it"],
                value="llama3-70b-8192",
                label="Select Groq Model"
            )
        with gr.Column(scale=2, min_width=600):
            with gr.Tabs():
                with gr.TabItem("PDF Viewer"):
                    with gr.Column(elem_classes="pdf-viewer-container"):
                        page_slider = gr.Slider(minimum=1, maximum=1, step=1, label="Page Number", value=1)
                        pdf_image = gr.Image(label="PDF Page", type="pil", elem_classes="pdf-viewer-image")
                        stats_display = gr.Markdown("No PDF uploaded yet", elem_classes="stats-box")
    
    with gr.Row(elem_classes="container"):
        with gr.Column(scale=2, min_width=600):
            chatbot = gr.Chatbot(height=500, bubble_full_width=False, show_copy_button=True, elem_classes="chat-container")
            with gr.Row():
                msg = gr.Textbox(show_label=False, placeholder="Ask about your technical document...", scale=5)
                send_btn = gr.Button("Send", scale=1)
            clear_btn = gr.Button("Clear Conversation")
    
    # Event Handlers (unchanged)
    upload_button.click(
        process_pdf,
        inputs=[pdf_file],
        outputs=[current_session_id, pdf_status, pdf_state]
    ).then(
        update_pdf_viewer,
        inputs=[pdf_state],
        outputs=[page_slider, pdf_image, stats_display]
    )
    
    msg.submit(
        generate_response,
        inputs=[msg, current_session_id, model_dropdown, chatbot],
        outputs=[chatbot]
    ).then(lambda: "", None, [msg])
    
    send_btn.click(
        generate_response,
        inputs=[msg, current_session_id, model_dropdown, chatbot],
        outputs=[chatbot]
    ).then(lambda: "", None, [msg])
    
    clear_btn.click(
        lambda: ([], None, "No PDF uploaded yet", {"page_images": [], "total_pages": 0, "total_words": 0}, 0, None, "No PDF uploaded yet"),
        None,
        [chatbot, current_session_id, pdf_status, pdf_state, page_slider, pdf_image, stats_display]
    )
    
    page_slider.change(
        update_image,
        inputs=[page_slider, pdf_state],
        outputs=[pdf_image]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch()