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context-caching-gemini-pdf-qa

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from flask import Flask, request, jsonify, render_template_string
from flask_cors import CORS
from google import genai
from google.genai import types
import os
import io
import httpx
import uuid
from datetime import datetime, timezone, timedelta
from dotenv import load_dotenv
import json

# Load environment variables from a .env file
# This is useful for local development. In production on platforms like Hugging Face,
# you'll set these as environment variables directly in the settings.
load_dotenv()

app = Flask(__name__)
CORS(app)

# Initialize Gemini client
# The API key should be loaded from environment variables
api_key = os.getenv('GOOGLE_API_KEY')
if not api_key:
    print("Error: GOOGLE_API_KEY environment variable not set.")
    # In a real app, you might exit or raise an exception here.
    # For this example, we'll print an error but allow the app to start;
    # API calls will fail if the key is missing.
    # If running locally, make sure you have a .env file with GOOGLE_API_KEY=YOUR_API_KEY
    pass # Allows the app to run without a key for debugging non-API parts

try:
    client = genai.Client(api_key=api_key)
except Exception as e:
    print(f"Failed to initialize Gemini client: {e}")
    client = None # Set client to None if initialization fails

# In-memory storage for demo (in production, use a database like Redis or PostgreSQL)
# Maps our internal cache_id (UUID) to Gemini's cache_name and other info
document_caches = {}
user_sessions = {} # Not used in this version, but kept from template

# HTML template for the web interface
HTML_TEMPLATE = """
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Smart Document Analysis Platform</title>
    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
    <style>
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }

        body {
            font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            min-height: 100vh;
            color: #333;
            line-height: 1.6;
        }

        .container {
            max-width: 1400px;
            margin: 0 auto;
            padding: 20px;
            min-height: 100vh;
            display: flex;
            flex-direction: column;
        }

        .header {
            text-align: center;
            margin-bottom: 30px;
            color: white;
        }

        .header h1 {
            font-size: 2.8em;
            font-weight: 700;
            margin-bottom: 10px;
            text-shadow: 0 2px 4px rgba(0,0,0,0.3);
        }

        .header p {
            font-size: 1.2em;
            opacity: 0.9;
            font-weight: 300;
        }

        .main-content {
            display: grid;
            grid-template-columns: 1fr 1fr;
            gap: 30px;
            flex-grow: 1;
        }

        .left-panel, .right-panel {
            background: white;
            border-radius: 20px;
            padding: 30px;
            box-shadow: 0 20px 40px rgba(0,0,0,0.1);
            display: flex;
            flex-direction: column;
        }

        .left-panel {
             overflow-y: auto; /* Allow scrolling if content is tall */
        }

        .panel-title {
            font-size: 1.5em;
            font-weight: 600;
            margin-bottom: 20px;
            color: #2d3748;
            display: flex;
            align-items: center;
            gap: 10px;
        }

        .upload-section {
            margin-bottom: 30px;
        }

        .upload-area {
            border: 2px dashed #667eea;
            border-radius: 15px;
            padding: 40px;
            text-align: center;
            background: #f8fafc;
            transition: all 0.3s ease;
            margin-bottom: 20px;
            cursor: pointer; /* Indicate clickable area */
        }

        .upload-area:hover {
            border-color: #764ba2;
            background: #f0f2ff;
            transform: translateY(-2px);
        }

        .upload-area.dragover {
            border-color: #764ba2;
            background: #e8f0ff;
            transform: scale(1.02);
        }

        .upload-icon {
            font-size: 3em;
            color: #667eea;
            margin-bottom: 15px;
        }

        .file-input {
            display: none;
        }

        .upload-btn {
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
            border: none;
            padding: 15px 30px;
            border-radius: 25px;
            cursor: pointer;
            font-size: 1.1em;
            font-weight: 500;
            transition: all 0.3s ease;
            margin: 10px;
        }

        .upload-btn:hover {
            transform: translateY(-2px);
            box-shadow: 0 10px 20px rgba(102, 126, 234, 0.3);
        }

        .url-input {
            width: 100%;
            padding: 15px;
            border: 2px solid #e2e8f0;
            border-radius: 10px;
            font-size: 1em;
            margin-bottom: 15px;
            transition: border-color 0.3s ease;
        }

        .url-input:focus {
            outline: none;
            border-color: #667eea;
        }

        .btn {
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
            border: none;
            padding: 12px 25px;
            border-radius: 20px;
            cursor: pointer;
            font-size: 1em;
            font-weight: 500;
            transition: all 0.3s ease;
        }

        .btn:hover {
            transform: translateY(-1px);
            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.3);
        }

        .btn:disabled {
            opacity: 0.6;
            cursor: not-allowed;
            transform: none;
        }

        .chat-container {
            flex: 1;
            border: 1px solid #e2e8f0;
            border-radius: 15px;
            overflow-y: auto;
            padding: 20px;
            background: #f8fafc;
            margin-bottom: 20px;
            display: flex;
            flex-direction: column;
        }

        .message {
            margin-bottom: 15px;
            padding: 15px;
            border-radius: 12px;
            max-width: 85%;
            animation: fadeIn 0.3s ease;
            word-wrap: break-word; /* Ensure long words wrap */
        }

        @keyframes fadeIn {
            from { opacity: 0; transform: translateY(10px); }
            to { opacity: 1; transform: translateY(0); }
        }

        .user-message {
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
            margin-left: auto;
            box-shadow: 0 4px 12px rgba(102, 126, 234, 0.3);
        }

        .ai-message {
            background: white;
            color: #333;
            border: 1px solid #e2e8f0;
            box-shadow: 0 2px 8px rgba(0,0,0,0.1);
            margin-right: auto; /* Align AI messages to the left */
        }

        .input-group {
            display: flex;
            gap: 10px;
        }

        .question-input {
            flex: 1;
            padding: 15px;
            border: 2px solid #e2e8f0;
            border-radius: 12px;
            font-size: 1em;
            transition: border-color 0.3s ease;
        }

        .question-input:focus {
            outline: none;
            border-color: #667eea;
        }

        .cache-info {
            background: linear-gradient(135deg, #48bb78 0%, #38a169 100%);
            border-radius: 12px;
            padding: 20px;
            margin-bottom: 20px;
            color: white;
            box-shadow: 0 4px 12px rgba(72, 187, 120, 0.3);
        }

        .cache-info h3 {
            margin-bottom: 10px;
            font-weight: 600;
        }

         .cache-info p {
             font-size: 0.9em;
             margin-bottom: 5px;
         }

         .cache-info p:last-child {
             margin-bottom: 0;
         }


        .loading {
            text-align: center;
            padding: 40px;
            color: #666;
        }

        .loading-spinner {
            border: 3px solid #f3f3f3;
            border-top: 3px solid #667eea;
            border-radius: 50%;
            width: 40px;
            height: 40px;
            animation: spin 1s linear infinite;
            margin: 0 auto 20px;
        }

        @keyframes spin {
            0% { transform: rotate(0deg); }
            100% { transform: rotate(360deg); }
        }

        .error {
            background: linear-gradient(135deg, #f56565 0%, #e53e3e 100%);
            border-radius: 12px;
            padding: 15px;
            color: white;
            margin-bottom: 20px;
            box-shadow: 0 4px 12px rgba(245, 101, 101, 0.3);
        }

        .success {
            background: linear-gradient(135deg, #48bb78 0%, #38a169 100%);
            border-radius: 12px;
            padding: 15px;
            color: white;
            margin-bottom: 20px;
            box-shadow: 0 4px 12px rgba(72, 187, 120, 0.3);
        }

        @media (max-width: 768px) {
            .main-content {
                grid-template-columns: 1fr;
                gap: 20px;
            }

            .header h1 {
                font-size: 2em;
            }
        }
    </style>
</head>
<body>
    <div class="container">
        <div class="header">
            <h1>📚 Smart Document Analysis Platform</h1>
            <p>Upload PDF documents once, ask questions forever with Gemini API caching</p>
             <p style="font-size:0.9em; margin-top: 5px; opacity: 0.8;">Powered by Google Gemini API - Explicit Caching</p>
        </div>

        <div class="main-content">
            <!-- Left Panel - Upload Section -->
            <div class="left-panel">
                <div class="panel-title">
                    📤 Upload PDF Document
                </div>

                <div class="upload-section">
                    <div class="upload-area" id="uploadArea">
                        <div class="upload-icon">📄</div>
                        <p>Drag and drop your PDF file here, or click to select</p>
                        <input type="file" id="fileInput" class="file-input" accept=".pdf">
                         <!-- The button triggers the hidden file input -->
                        <button type="button" class="upload-btn" onclick="document.getElementById('fileInput').click()">
                            Choose PDF File
                        </button>
                    </div>

                    <div style="margin-top: 20px;">
                        <h3>Or provide a URL:</h3>
                        <input type="url" id="urlInput" class="url-input" placeholder="https://example.com/document.pdf">
                        <button type="button" class="btn" onclick="uploadFromUrl()">Upload from URL</button>
                    </div>
                </div>

                <div id="loading" class="loading" style="display: none;">
                    <div class="loading-spinner"></div>
                    <p id="loadingText">Processing your PDF... This may take a moment.</p>
                </div>

                <div id="error" class="error" style="display: none;"></div>
                <div id="success" class="success" style="display: none;"></div>
            </div>

            <!-- Right Panel - Chat Section -->
            <div class="right-panel">
                <div class="panel-title">
                    💬 Ask Questions
                </div>

                <div id="cacheInfo" class="cache-info" style="display: none;">
                    <h3>✅ Document Cached Successfully!</h3>
                    <p>Your PDF has been cached using Gemini API. You can now ask multiple questions without re-uploading.</p>
                    <p><strong>Cache ID:</strong> <span id="cacheId"></span></p>
                    <p><strong>Tokens Cached:</strong> <span id="tokenCount"></span></p>
                    <p>Note: Caching is ideal for larger documents (typically 1024+ tokens required).</p>
                </div>

                <div class="chat-container" id="chatContainer">
                    <div class="message ai-message">
                        👋 Hello! Upload a PDF document using the panel on the left, and I'll help you analyze it using Gemini API caching!
                    </div>
                </div>

                <div class="input-group">
                    <input type="text" id="questionInput" class="question-input" placeholder="Ask a question about your document..." disabled>
                    <button type="button" class="btn" onclick="askQuestion()" id="askBtn" disabled>Ask</button>
                </div>
            </div>
        </div>
    </div>
    <script>
        let currentCacheId = null;

        // Disable input/button initially
        document.getElementById('questionInput').disabled = true;
        document.getElementById('askBtn').disabled = true;

        // File upload handling
        const uploadArea = document.getElementById('uploadArea');
        const fileInput = document.getElementById('fileInput');

        // Prevent default drag behaviors
        ['dragenter', 'dragover', 'dragleave', 'drop'].forEach(eventName => {
            uploadArea.addEventListener(eventName, preventDefaults, false);
        });

        function preventDefaults (e) {
            e.preventDefault();
            e.stopPropagation();
        }

        // Highlight drop area when item is dragged over
        ['dragenter', 'dragover'].forEach(eventName => {
            uploadArea.addEventListener(eventName, () => uploadArea.classList.add('dragover'), false);
        });

        ['dragleave', 'drop'].forEach(eventName => {
            uploadArea.addEventListener(eventName, () => uploadArea.classList.remove('dragover'), false);
        });

        // Handle dropped files
        uploadArea.addEventListener('drop', handleDrop, false);

        function handleDrop(e) {
            const dt = e.dataTransfer;
            const files = dt.files;
            if (files.length > 0) {
                uploadFile(files[0]);
            }
        }

        fileInput.addEventListener('change', (e) => {
            if (e.target.files.length > 0) {
                uploadFile(e.target.files[0]);
                // Clear the input so the same file can be selected again if needed
                e.target.value = '';
            }
        });

        async function uploadFile(file) {
            if (!file.type.includes('pdf')) {
                showError('Please select a PDF file.');
                return;
            }

            // Clear previous status messages
            hideError();
            hideSuccess();
            document.getElementById('cacheInfo').style.display = 'none'; // Hide old cache info
            currentCacheId = null; // Clear old cache ID

            showLoading('Uploading PDF...');

            const formData = new FormData();
            formData.append('file', file);

            try {
                const response = await fetch('/upload', {
                    method: 'POST',
                    body: formData
                });

                const result = await response.json();

                if (result.success) {
                    currentCacheId = result.cache_id;
                    document.getElementById('cacheId').textContent = result.cache_id;
                    document.getElementById('tokenCount').textContent = result.token_count;
                    document.getElementById('cacheInfo').style.display = 'block';
                    showSuccess('PDF uploaded and cached successfully! You can now ask questions.');

                    // Enable chat input and button
                    document.getElementById('questionInput').disabled = false;
                    document.getElementById('askBtn').disabled = false;
                     document.getElementById('questionInput').focus(); // Focus input

                    // Add initial message
                    addMessage("I've analyzed your PDF document. What would you like to know about it?", 'ai');

                } else {
                    showError(result.error);
                     // Disable chat input/button if upload/cache failed
                    document.getElementById('questionInput').disabled = true;
                    document.getElementById('askBtn').disabled = true;
                }
            } catch (error) {
                showError('Error uploading file: ' + error.message);
                 // Disable chat input/button on network/server error
                document.getElementById('questionInput').disabled = true;
                document.getElementById('askBtn').disabled = true;
            } finally {
                hideLoading();
            }
        }

        async function uploadFromUrl() {
            const url = document.getElementById('urlInput').value;
            if (!url.trim()) {
                showError('Please enter a valid URL.');
                return;
            }

            // Clear previous status messages
            hideError();
            hideSuccess();
            document.getElementById('cacheInfo').style.display = 'none'; // Hide old cache info
            currentCacheId = null; // Clear old cache ID

            showLoading('Uploading PDF from URL...');

            try {
                const response = await fetch('/upload-url', {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/json'
                    },
                    body: JSON.stringify({ url: url })
                });

                const result = await response.json();

                if (result.success) {
                    currentCacheId = result.cache_id;
                    document.getElementById('cacheId').textContent = result.cache_id;
                    document.getElementById('tokenCount').textContent = result.token_count;
                    document.getElementById('cacheInfo').style.display = 'block';
                    showSuccess('PDF uploaded and cached successfully! You can now ask questions.');

                    // Enable chat input and button
                    document.getElementById('questionInput').disabled = false;
                    document.getElementById('askBtn').disabled = false;
                     document.getElementById('questionInput').focus(); // Focus input

                    // Add initial message
                    addMessage("I've analyzed your PDF document. What would you like to know about it?", 'ai');

                } else {
                    showError(result.error);
                     // Disable chat input/button if upload/cache failed
                    document.getElementById('questionInput').disabled = true;
                    document.getElementById('askBtn').disabled = true;
                }
            } catch (error) {
                showError('Error uploading from URL: ' + error.message);
                 // Disable chat input/button on network/server error
                document.getElementById('questionInput').disabled = true;
                document.getElementById('askBtn').disabled = false; // Should be false? Fix: should be true
            } finally {
                hideLoading();
            }
        }

        async function askQuestion() {
            const questionInput = document.getElementById('questionInput');
            const question = questionInput.value.trim();
            if (!question) return; // Don't send empty questions

            if (!currentCacheId) {
                showError('Please upload a PDF document first.');
                return;
            }

            // Add user message to chat
            addMessage(question, 'user');
            questionInput.value = ''; // Clear input immediately

            // Show loading state
            const askBtn = document.getElementById('askBtn');
            const originalText = askBtn.textContent;
            askBtn.textContent = 'Generating...';
            askBtn.disabled = true;
             questionInput.disabled = true; // Disable input while generating

            try {
                const response = await fetch('/ask', {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/json'
                    },
                    body: JSON.stringify({
                        question: question,
                        cache_id: currentCacheId // Use our internal cache_id
                    })
                });

                const result = await response.json();

                if (result.success) {
                    addMessage(result.answer, 'ai');
                } else {
                    addMessage('Error: ' + result.error, 'ai');
                }
            } catch (error) {
                addMessage('Error: ' + error.message, 'ai');
            } finally {
                askBtn.textContent = originalText;
                askBtn.disabled = false;
                questionInput.disabled = false; // Re-enable input
                questionInput.focus(); // Put focus back on input
                // Ensure button is disabled only if no cache is active
                if (!currentCacheId) {
                    askBtn.disabled = true;
                    questionInput.disabled = true;
                }
            }
        }

        function addMessage(text, sender) {
            const chatContainer = document.getElementById('chatContainer');
            const messageDiv = document.createElement('div');
            messageDiv.className = `message ${sender}-message`;

            // Use innerHTML to handle potential formatting like newlines or markdown
            // (Basic textContent might be sufficient depending on expected AI output)
            // For simplicity here, sticking to textContent as AI might output plain text
            messageDiv.textContent = text;

            // Basic handling for newlines
            messageDiv.style.whiteSpace = 'pre-wrap';

            chatContainer.appendChild(messageDiv);
            chatContainer.scrollTop = chatContainer.scrollHeight; // Auto-scroll to latest message
        }

        function showLoading(text = 'Processing...') {
            document.getElementById('loadingText').textContent = text;
            document.getElementById('loading').style.display = 'block';
        }

        function hideLoading() {
            document.getElementById('loading').style.display = 'none';
        }

        function showError(message) {
            const errorDiv = document.getElementById('error');
            errorDiv.textContent = message;
            errorDiv.style.display = 'block';
            // Auto-hide after 5 seconds
            setTimeout(() => {
                errorDiv.style.display = 'none';
            }, 5000);
        }

        function showSuccess(message) {
            const successDiv = document.getElementById('success');
            successDiv.textContent = message;
            successDiv.style.display = 'block';
             // Auto-hide after 5 seconds
            setTimeout(() => {
                successDiv.style.display = 'none';
            }, 5000);
        }

        function hideError() {
             document.getElementById('error').style.display = 'none';
        }

        function hideSuccess() {
             document.getElementById('success').style.display = 'none';
        }

        // Enter key to ask question
        document.getElementById('questionInput').addEventListener('keypress', (e) => {
            // Check if the input is not disabled and the key is Enter
            if (!document.getElementById('questionInput').disabled && e.key === 'Enter') {
                e.preventDefault(); // Prevent default form submission if input is part of a form
                askQuestion();
            }
        });

        // Initial message visibility
        // addMessage("👋 Hello! Upload a PDF document using the panel on the left, and I'll help you analyze it using Gemini API caching!", 'ai'); // Added this directly in HTML
    </script>
</body>
</html>
"""

# --- Flask Routes ---

@app.route('/')
def index():
    # Ensure API key is set before rendering, or add a warning to the template
    if not api_key:
        # You could modify the template or pass a variable to indicate error state
        print("Warning: API key not set. API calls will fail.")
    return render_template_string(HTML_TEMPLATE)

@app.route('/health', methods=['GET'])
def health_check():
    # A simple endpoint to check if the application is running
    # Can optionally check API client status if needed, but basic 200 is common.
    if client is None and api_key is not None: # Client failed to initialize despite key being present
        return jsonify({"status": "unhealthy", "reason": "Gemini client failed to initialize"}), 500
    # Note: This doesn't check if the API key is *valid* or if the API is reachable,
    # just if the Flask app is running and the client object was created.
    return jsonify({"status": "healthy"}), 200


@app.route('/upload', methods=['POST'])
def upload_file():
    if client is None or api_key is None:
         return jsonify({'success': False, 'error': 'API key not configured or Gemini client failed to initialize.'}), 500

    try:
        if 'file' not in request.files:
            return jsonify({'success': False, 'error': 'No file provided'})

        file = request.files['file']

        if file.filename == '':
            return jsonify({'success': False, 'error': 'No file selected'})

        # Read file content
        file_content = file.read()
        file_io = io.BytesIO(file_content)

        # --- CORRECTED FILE UPLOAD CALL ---
        # Upload to Gemini File API using the correct method client.upload_file
        # Pass the file content as a tuple (filename, file-like object, mime_type)
        # This replaces the incorrect client.files.upload call
        document = None # Initialize document variable
        try:
            # The mime_type is crucial for the API to correctly process the file.
            # The filename is used as the display_name by default if not provided.
            document = client.upload_file(
                file=(file.filename, file_io, 'application/pdf'), # Use the 'file' argument with tuple format
                # display_name=file.filename # Optional: explicitly provide a display name
            )
            print(f"File uploaded successfully to Gemini File API: {document.name}") # Log for debugging
            # Note: client.upload_file returns a google.generativeai.types.File object
            # which contains the resource name (e.g., 'files/xyz123').
        except Exception as upload_error:
             # Attempt to provide more specific feedback if possible
             error_msg = str(upload_error)
             print(f"Error uploading file to Gemini API: {error_msg}")
             # Check for common upload errors like exceeding file size limits
             if "file content size exceeds limit" in error_msg.lower():
                  return jsonify({'success': False, 'error': f'Error uploading file: File size exceeds API limit. {error_msg}'}), 413 # 413 Payload Too Large
             return jsonify({'success': False, 'error': f'Error uploading file to Gemini API: {error_msg}'}), 500
        # --- END CORRECTED FILE UPLOAD CALL ---

        # Create cache with system instruction
        cache = None # Initialize cache variable
        try:
            system_instruction = "You are an expert document analyzer. Provide detailed, accurate answers based on the uploaded document content. Always be helpful and thorough in your responses."

            # Use the correct model format as per documentation
            # Using a specific stable version is recommended for production
            model = 'models/gemini-2.0-flash-001'

            print(f"Attempting to create cache for file: {document.name}") # Log
            cache = client.caches.create(
                model=model,
                config=types.CreateCachedContentConfig(
                    display_name=f'pdf document cache: {file.filename}', # Use filename in display_name
                    system_instruction=system_instruction,
                    contents=[document], # contents should be a list of content parts. document is already a File object, which is a valid content part type.
                    ttl="3600s",  # 1 hour TTL. Use string format like "300s" or "1h".
                )
            )
            print(f"Cache created successfully: {cache.name}") # Log

            # Store cache info in our in-memory dictionary
            # We map our internal UUID cache_id to the Gemini API's cache.name (resource name)
            cache_id = str(uuid.uuid4())
            document_caches[cache_id] = {
                'gemini_cache_name': cache.name, # Store the Gemini API resource name
                'document_name': file.filename,
                'gemini_file_name': document.name, # Also store the Gemini File API resource name for cleanup
                'created_at': datetime.now().isoformat(),
                'expires_at': (datetime.now(timezone.utc) + timedelta(seconds=3600)).isoformat(), # Store expiry time for reference
            }

            # Get token count from cache metadata if available
            # Note: cached_token_count might be available on the cache object after creation
            token_count = 'Unknown'
            if hasattr(cache, 'usage_metadata') and cache.usage_metadata:
                 token_count = getattr(cache.usage_metadata, 'cached_token_count', 'Unknown')
                 print(f"Cached token count: {token_count}")


            return jsonify({
                'success': True,
                'cache_id': cache_id, # Return our internal ID
                'token_count': token_count
            })

        except Exception as cache_error:
            error_msg = str(cache_error)
            print(f"Cache creation failed: {error_msg}") # Log the cache error
            # If caching fails, attempt to delete the uploaded file to clean up.
            if document and hasattr(document, 'name'):
                 try:
                     client.files.delete(document.name)
                     print(f"Cleaned up uploaded file {document.name} after caching failure.")
                 except Exception as cleanup_error:
                      print(f"Failed to clean up file {document.name}: {cleanup_error}")

            # Handle specific cache creation errors
            # Note: The exact error message for content size can vary or might not be specific
            # The documentation mentions minimum tokens for caching.
            if "Cached content is too small" in error_msg or "minimum size" in error_msg.lower() or "tokens required" in error_msg.lower():
                 return jsonify({
                     'success': False,
                     'error': f'PDF content is too small for caching. Minimum token count varies by model, but is typically 1024+ for Flash. {error_msg}',
                     'suggestion': 'Try uploading a longer document or combine multiple documents.'
                 }), 400 # 400 Bad Request - client error
            else:
                # Re-raise other unexpected errors or return a generic error
                 return jsonify({'success': False, 'error': f'Error creating cache with Gemini API: {error_msg}'}), 500


    except Exception as e:
        print(f"An unexpected error occurred during upload process: {str(e)}") # Log general errors
        return jsonify({'success': False, 'error': str(e)}), 500

@app.route('/upload-url', methods=['POST'])
def upload_from_url():
    if client is None or api_key is None:
         return jsonify({'success': False, 'error': 'API key not configured or Gemini client failed to initialize.'}), 500

    try:
        data = request.get_json()
        url = data.get('url')

        if not url:
            return jsonify({'success': False, 'error': 'No URL provided'}), 400 # 400 Bad Request

        # Download file from URL
        response = None
        try:
            # Use stream=True for potentially large files, although httpx handles it well.
            # Add a timeout to prevent hanging on unresponsive URLs.
            response = httpx.get(url, follow_redirects=True, timeout=30.0)
            response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)

             # Basic check for PDF mime type (optional but good practice)
            content_type = response.headers.get('Content-Type', '').lower()
            if 'application/pdf' not in content_type:
                 print(f"Warning: URL content type is not application/pdf: {content_type}")
                 # Decide if you want to block non-PDFs or try to upload anyway
                 # For now, we'll proceed but log a warning. API might reject it.
                 # If strictly PDF required, return an error here:
                 # return jsonify({'success': False, 'error': f'URL does not point to a PDF document (Content-Type: {content_type})'}), 415 # 415 Unsupported Media Type


        except httpx.HTTPStatusError as e:
             print(f"HTTP error downloading file from URL {url}: {e.response.status_code} - {e.response.text}")
             return jsonify({'success': False, 'error': f'HTTP error downloading file from URL: {e.response.status_code} - {e.response.text}'}), e.response.status_code
        except httpx.RequestError as e:
             print(f"Error downloading file from URL {url}: {e}")
             return jsonify({'success': False, 'error': f'Error downloading file from URL: {e}'}), 500


        file_io = io.BytesIO(response.content)

        # --- CORRECTED FILE UPLOAD CALL ---
        # Upload to Gemini File API using the correct method client.upload_file
        # Pass the file content as a tuple (filename, file-like object, mime_type)
        # Use a generic filename for the file-like object if none derived from URL
        document = None # Initialize document variable
        try:
            # Attempt to get filename from URL or headers, otherwise use generic
            filename = os.path.basename(url)
            if not filename or '.' not in filename:
                 filename = 'downloaded_document.pdf' # Default generic name

            # Use the mime type from the response headers if available and looks right
            mime_type = content_type if 'application/pdf' in content_type else 'application/pdf'


            document = client.upload_file(
                file=(filename, file_io, mime_type), # Use parsed filename and mime_type
                display_name=url # Use the URL as display name in Gemini API
            )
            print(f"File from URL uploaded successfully to Gemini File API: {document.name}") # Log
             # Note: client.upload_file returns a google.generativeai.types.File object
             # which contains the resource name (e.g., 'files/xyz123').

        except Exception as upload_error:
             # Attempt to provide more specific feedback if possible
             error_msg = str(upload_error)
             print(f"Error uploading file from URL to Gemini API: {error_msg}")
             # Check for common upload errors like exceeding file size limits
             if "file content size exceeds limit" in error_msg.lower():
                  return jsonify({'success': False, 'error': f'Error uploading file: File size exceeds API limit. {error_msg}'}), 413 # 413 Payload Too Large
             return jsonify({'success': False, 'error': f'Error uploading file from URL to Gemini API: {error_msg}'}), 500
        # --- END CORRECTED FILE UPLOAD CALL ---


        # Create cache with system instruction
        cache = None # Initialize cache variable
        try:
            system_instruction = "You are an expert document analyzer. Provide detailed, accurate answers based on the uploaded document content. Always be helpful and thorough in your responses."

            # Use the correct model format as per documentation
            model = 'models/gemini-2.0-flash-001'

            print(f"Attempting to create cache for file: {document.name}") # Log
            cache = client.caches.create(
                model=model,
                config=types.CreateCachedContentConfig(
                    display_name=f'pdf document cache: {url}', # Use URL in display_name for cache
                    system_instruction=system_instruction,
                    contents=[document], # contents should be a list containing the File object
                    ttl="3600s",  # 1 hour TTL. Use string format like "300s" or "1h".
                )
            )
            print(f"Cache created successfully: {cache.name}") # Log


            # Store cache info in our in-memory dictionary
            # We map our internal UUID cache_id to the Gemini API's cache.name (resource name)
            cache_id = str(uuid.uuid4())
            document_caches[cache_id] = {
                'gemini_cache_name': cache.name, # Store the Gemini API resource name
                'document_name': url, # Store the URL as the document name
                'gemini_file_name': document.name, # Also store the Gemini File API resource name for cleanup
                'created_at': datetime.now().isoformat(),
                 'expires_at': (datetime.now(timezone.utc) + timedelta(seconds=3600)).isoformat(), # Store expiry time for reference
            }

            # Get token count from cache metadata if available
            token_count = 'Unknown'
            if hasattr(cache, 'usage_metadata') and cache.usage_metadata:
                 token_count = getattr(cache.usage_metadata, 'cached_token_count', 'Unknown')
                 print(f"Cached token count: {token_count}")


            return jsonify({
                'success': True,
                'cache_id': cache_id, # Return our internal ID
                'token_count': token_count
            })

        except Exception as cache_error:
            error_msg = str(cache_error)
            print(f"Cache creation failed: {error_msg}") # Log the cache error
            # If caching fails, attempt to delete the uploaded file to clean up.
            if document and hasattr(document, 'name'):
                 try:
                     client.files.delete(document.name)
                     print(f"Cleaned up uploaded file {document.name} after caching failure.")
                 except Exception as cleanup_error:
                      print(f"Failed to clean up file {document.name}: {cleanup_error}")

            # Handle specific cache creation errors
            if "Cached content is too small" in error_msg or "minimum size" in error_msg.lower() or "tokens required" in error_msg.lower():
                 return jsonify({
                     'success': False,
                     'error': f'PDF content is too small for caching. Minimum token count varies by model, but is typically 1024+ for Flash. {error_msg}',
                     'suggestion': 'Try uploading a longer document or combine multiple documents.'
                 }), 400 # 400 Bad Request - client error
            else:
                # Re-raise other unexpected errors or return a generic error
                 return jsonify({'success': False, 'error': f'Error creating cache with Gemini API: {error_msg}'}), 500


    except Exception as e:
        print(f"An unexpected error occurred during URL upload process: {str(e)}") # Log general errors
        return jsonify({'success': False, 'error': str(e)}), 500


@app.route('/ask', methods=['POST'])
def ask_question():
    if client is None or api_key is None:
         return jsonify({'success': False, 'error': 'API key not configured or Gemini client failed to initialize.'}), 500

    try:
        data = request.get_json()
        question = data.get('question')
        cache_id = data.get('cache_id')

        if not question or not cache_id:
            return jsonify({'success': False, 'error': 'Missing question or cache_id'}), 400 # 400 Bad Request

        # --- CORRECTED CACHE LOOKUP ---
        # Check if our internal cache_id exists in the in-memory dictionary
        if cache_id not in document_caches:
            # If not found, it's either an invalid ID, expired, or the server restarted.
            # For this simple demo, we treat it as unavailable.
            print(f"Cache ID {cache_id} not found in local storage.")
            return jsonify({'success': False, 'error': 'Cache not found or expired. Please upload the document again.'}), 404 # 404 Not Found

        # If found, retrieve the Gemini API cache name
        cache_info = document_caches[cache_id]
        gemini_cache_name = cache_info['gemini_cache_name']
        print(f"Using Gemini cache name: {gemini_cache_name} for question.")
        # --- END CORRECTED CACHE LOOKUP ---

        # Generate response using cached content with correct model format
        response = client.models.generate_content(
            model='models/gemini-2.0-flash-001', # Ensure using the model the cache was created with
            contents=[{'text': question}], # User's question as text content part
            generation_config=types.GenerateContentConfig(
                cached_content=gemini_cache_name # Use the retrieved Gemini cache name
            )
        )

        # Check if response has parts before accessing .text
        answer = "Could not generate response from the model."
        if response and response.candidates:
            # Handle potential tool_code or other non-text parts if necessary
            answer_parts = []
            for candidate in response.candidates:
                if candidate.content and candidate.content.parts:
                    for part in candidate.content.parts:
                        if hasattr(part, 'text'):
                             answer_parts.append(part.text)
                        # Add handling for other part types if needed (e.g., tool_code, function_response)
                        # elif hasattr(part, 'tool_code'):
                        #      answer_parts.append(f"\n```tool_code\n{part.tool_code.code}\n```\n")
                        # elif hasattr(part, 'function_response'):
                        #      answer_parts.append(f"\n```function_response\n{json.dumps(part.function_response, indent=2)}\n```\n")
            if answer_parts:
                 answer = "".join(answer_parts)
            else:
                 # Handle cases where candidates exist but have no text parts (e.g., tool calls)
                 answer = "Model returned content without text parts (e.g., tool calls)."
                 print(f"Model returned non-text parts: {response.candidates}") # Log for debugging

        elif response and response.prompt_feedback and response.prompt_feedback.block_reason:
             # Handle cases where the prompt was blocked
             block_reason = response.prompt_feedback.block_reason.name
             block_message = getattr(response.prompt_feedback, 'block_reason_message', 'No message provided')
             answer = f"Request blocked by safety filters. Reason: {block_reason}. Message: {block_message}"
             print(f"Request blocked: {block_reason} - {block_message}")

        else:
             # Handle other unexpected response structures
             print(f"Unexpected response structure from API: {response}")
             # answer stays as the initial "Could not generate response..." message

        return jsonify({
            'success': True,
            'answer': answer
        })

    except Exception as e:
        print(f"An error occurred during question asking: {str(e)}") # Log errors
        # Attempt to provide more specific API error messages
        error_msg = str(e)
        if "Resource has been exhausted" in error_msg:
             error_msg = "API rate limit or quota exceeded. Please try again later."
        elif "cached_content refers to a resource that has been deleted" in error_msg:
             error_msg = "The cached document has expired or was deleted from Gemini API. Please upload the document again."
             # Clean up local entry if API confirms deletion/expiry
             if cache_id in document_caches:
                  print(f"Removing local entry for cache_id {cache_id} as API confirmed deletion.")
                  del document_caches[cache_id]
        elif "invalid cached_content value" in error_msg:
             error_msg = "Invalid cache reference. The cached document might have expired or been deleted. Please upload the document again."
             # Clean up local entry if API confirms deletion/expiry
             if cache_id in document_caches:
                  print(f"Removing local entry for cache_id {cache_id} as API confirmed deletion (invalid reference).")
                  del document_caches[cache_id]
        elif "model does not exist" in error_msg:
             error_msg = "The specified model is not available."


        return jsonify({'success': False, 'error': f'Error from Gemini API: {error_msg}'}), 500 # 500 Internal Server Error


@app.route('/caches', methods=['GET'])
def list_caches():
    # Lists caches stored *in this application's memory*.
    # It does NOT list caches directly from the Gemini API unless you add that logic.
    try:
        caches = []
        for cache_id, cache_info in list(document_caches.items()): # Use list() to iterate safely if modification occurs during iteration
             # Optional: Check if the cache still exists in Gemini API before listing
             # This adds complexity and potential API calls, so skipping for simple demo
             try:
                  # Attempt to get cache metadata from API to confirm existence/details
                  api_cache_info = client.caches.get(name=cache_info['gemini_cache_name'])
                  # If successful, add to list
                  caches.append({
                       'cache_id': cache_id, # Our internal ID
                       'document_name': cache_info['document_name'],
                       'gemini_cache_name': cache_info['gemini_cache_name'], # Include Gemini name
                       'created_at': cache_info['created_at'],
                       'expires_at': getattr(api_cache_info, 'expire_time', 'Unknown'), # Get actual expiry from API
                       'cached_token_count': getattr(api_cache_info.usage_metadata, 'cached_token_count', 'Unknown') if hasattr(api_cache_info, 'usage_metadata') else 'Unknown'
                  })
             except Exception as e:
                  # If API lookup fails (e.g., cache expired/deleted), remove from our local map
                  print(f"Gemini cache {cache_info['gemini_cache_name']} for local ID {cache_id} not found via API. Removing from local storage. Error: {e}")
                  del document_caches[cache_id]
                  # Don't add it to the list of active caches

        return jsonify({'success': True, 'caches': caches})

    except Exception as e:
        print(f"An error occurred listing caches: {str(e)}")
        return jsonify({'success': False, 'error': str(e)})


@app.route('/cache/<cache_id>', methods=['DELETE'])
def delete_cache(cache_id):
    if client is None or api_key is None:
         return jsonify({'success': False, 'error': 'API key not configured or Gemini client failed to initialize.'}), 500

    try:
        if cache_id not in document_caches:
            return jsonify({'success': False, 'error': 'Cache not found'}), 404 # 404 Not Found

        cache_info = document_caches[cache_id]
        gemini_cache_name_to_delete = cache_info['gemini_cache_name']
        gemini_file_name_to_delete = cache_info['gemini_file_name']


        # Delete from Gemini API Cache Service
        try:
            client.caches.delete(gemini_cache_name_to_delete)
            print(f"Gemini cache deleted: {gemini_cache_name_to_delete}") # Log
        except Exception as delete_error:
             error_msg = str(delete_error)
             print(f"Error deleting Gemini cache {gemini_cache_name_to_delete}: {error_msg}") # Log
             # Handle case where the cache was already gone (e.g. expired)
             if "Resource not found" in error_msg:
                  print(f"Gemini cache {gemini_cache_name_to_delete} already gone from API.")
             else:
                  # For other errors, you might want to stop and return the error
                  return jsonify({'success': False, 'error': f'Failed to delete cache from API: {error_msg}'}), 500


        # Also delete the associated file from Gemini File API to free up storage
        if gemini_file_name_to_delete:
             try:
                 client.files.delete(gemini_file_name_to_delete)
                 print(f"Associated Gemini file deleted: {gemini_file_name_to_delete}") # Log
             except Exception as file_delete_error:
                 error_msg = str(file_delete_error)
                 print(f"Error deleting Gemini file {gemini_file_name_to_delete}: {error_msg}") # Log
                 if "Resource not found" in error_msg:
                      print(f"Gemini file {gemini_file_name_to_delete} already gone from API.")
                 else:
                      # Log but continue, deleting the cache is the primary goal
                      pass


        # Remove from local storage *after* attempting API deletion
        del document_caches[cache_id]
        print(f"Local cache entry deleted for ID: {cache_id}") # Log

        return jsonify({'success': True, 'message': 'Cache and associated file deleted successfully'})

    except Exception as e:
        print(f"An unexpected error occurred during cache deletion process: {str(e)}") # Log
        return jsonify({'success': False, 'error': str(e)}), 500


if __name__ == '__main__':
    import os
    port = int(os.environ.get("PORT", 7860))
    print(f"Starting Flask app on port {port}") # Log start
    # In production, set debug=False
    # Use threaded=True or a production WSGI server (like Gunicorn) for concurrent requests
    app.run(debug=True, host='0.0.0.0', port=port, threaded=True)