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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
load_dotenv()

app = Flask(__name__)
CORS(app)

# Initialize Gemini client
client = genai.Client(api_key=os.getenv('GOOGLE_API_KEY'))

# In-memory storage for demo (in production, use a database)
document_caches = {}
user_sessions = {}

# 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>
    <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;
        }
        
        .container {
            max-width: 1400px;
            margin: 0 auto;
            padding: 20px;
            min-height: 100vh;
        }
        
        .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;
            height: calc(100vh - 200px);
        }
        
        .left-panel {
            background: white;
            border-radius: 20px;
            padding: 30px;
            box-shadow: 0 20px 40px rgba(0,0,0,0.1);
            overflow-y: auto;
        }
        
        .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;
        }
        
        .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;
        }
        
        .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;
        }
        
        .message {
            margin-bottom: 15px;
            padding: 15px;
            border-radius: 12px;
            max-width: 85%;
            animation: fadeIn 0.3s ease;
        }
        
        @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);
        }
        
        .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;
        }
        
        .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>
        </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">
                        <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 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>
                </div>
                
                <div class="chat-container" id="chatContainer">
                    <div class="message ai-message">
                        👋 Hello! I'm ready to analyze your PDF documents. Upload a document to get started!
                    </div>
                </div>
                
                <div class="input-group">
                    <input type="text" id="questionInput" class="question-input" placeholder="Ask a question about your document...">
                    <button class="btn" onclick="askQuestion()" id="askBtn">Ask</button>
                </div>
            </div>
        </div>
    </div>

    <script>
        let currentCacheId = null;
        
        // File upload handling
        const uploadArea = document.getElementById('uploadArea');
        const fileInput = document.getElementById('fileInput');
        
        uploadArea.addEventListener('dragover', (e) => {
            e.preventDefault();
            uploadArea.classList.add('dragover');
        });
        
        uploadArea.addEventListener('dragleave', () => {
            uploadArea.classList.remove('dragover');
        });
        
        uploadArea.addEventListener('drop', (e) => {
            e.preventDefault();
            uploadArea.classList.remove('dragover');
            const files = e.dataTransfer.files;
            if (files.length > 0) {
                uploadFile(files[0]);
            }
        });
        
        fileInput.addEventListener('change', (e) => {
            if (e.target.files.length > 0) {
                uploadFile(e.target.files[0]);
            }
        });
        
        async function uploadFile(file) {
            if (!file.type.includes('pdf')) {
                showError('Please select a PDF file.');
                return;
            }
            
            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!');
                    
                    // Add initial message
                    addMessage("I've analyzed your PDF document. What would you like to know about it?", 'ai');
                } else {
                    showError(result.error);
                }
            } catch (error) {
                showError('Error uploading file: ' + error.message);
            } finally {
                hideLoading();
            }
        }
        
        async function uploadFromUrl() {
            const url = document.getElementById('urlInput').value;
            if (!url) {
                showError('Please enter a valid URL.');
                return;
            }
            
            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!');
                    
                    // Add initial message
                    addMessage("I've analyzed your PDF document. What would you like to know about it?", 'ai');
                } else {
                    showError(result.error);
                }
            } catch (error) {
                showError('Error uploading from URL: ' + error.message);
            } finally {
                hideLoading();
            }
        }
        
        async function askQuestion() {
            const question = document.getElementById('questionInput').value;
            if (!question.trim()) return;
            
            if (!currentCacheId) {
                showError('Please upload a PDF document first.');
                return;
            }
            
            // Add user message to chat
            addMessage(question, 'user');
            document.getElementById('questionInput').value = '';
            
            // Show loading state
            const askBtn = document.getElementById('askBtn');
            const originalText = askBtn.textContent;
            askBtn.textContent = 'Generating...';
            askBtn.disabled = true;
            
            try {
                const response = await fetch('/ask', {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/json'
                    },
                    body: JSON.stringify({
                        question: question,
                        cache_id: currentCacheId
                    })
                });
                
                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;
            }
        }
        
        function addMessage(text, sender) {
            const chatContainer = document.getElementById('chatContainer');
            const messageDiv = document.createElement('div');
            messageDiv.className = `message ${sender}-message`;
            messageDiv.textContent = text;
            chatContainer.appendChild(messageDiv);
            chatContainer.scrollTop = chatContainer.scrollHeight;
        }
        
        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';
            setTimeout(() => {
                errorDiv.style.display = 'none';
            }, 5000);
        }
        
        function showSuccess(message) {
            const successDiv = document.getElementById('success');
            successDiv.textContent = message;
            successDiv.style.display = 'block';
            setTimeout(() => {
                successDiv.style.display = 'none';
            }, 5000);
        }
        
        // Enter key to ask question
        document.getElementById('questionInput').addEventListener('keypress', (e) => {
            if (e.key === 'Enter') {
                askQuestion();
            }
        });
    </script>
</body>
</html>
"""

# ... (imports and initial setup) ...

@app.route('/')
def index():
    return render_template_string(HTML_TEMPLATE)

# Add health check endpoint
@app.route('/health', methods=['GET'])
def health_check():
    # A simple endpoint to check if the application is running
    return jsonify({"status": "healthy"}), 200

@app.route('/upload', methods=['POST'])
def upload_file():
    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
        try:
            document = client.upload_file(
                file=(file.filename, file_io, 'application/pdf'),
                display_name=file.filename # Optional: provide a display name
            )
            print(f"File uploaded successfully: {document.name}") # Log for debugging
        except Exception as upload_error:
             return jsonify({'success': False, 'error': f'Error uploading file to Gemini API: {str(upload_error)}'})
        # --- END CORRECTED FILE UPLOAD CALL ---

        # Create cache with system instruction
        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: {file.filename}', # Use filename in display_name
                    system_instruction=system_instruction,
                    contents=[document], # document is the File object returned by upload_file
                    ttl="3600s",  # 1 hour TTL
                )
            )
            print(f"Cache created successfully: {cache.name}") # Log

            # Store cache info
            cache_id = str(uuid.uuid4())
            document_caches[cache_id] = {
                'cache_name': cache.name,
                'document_name': file.filename,
                'created_at': datetime.now().isoformat()
            }

            # 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')

            return jsonify({
                'success': True,
                'cache_id': cache_id,
                'token_count': token_count
            })

        except Exception as cache_error:
            print(f"Cache creation failed: {str(cache_error)}") # Log the cache error
            # If caching fails due to small content, provide alternative approach
            # Note: The exact error message might vary, checking substring is a bit fragile
            # A better way might be to count tokens first, but requires API call
            if "Cached content is too small" in str(cache_error) or "minimum" in str(cache_error).lower():
                 # Attempt to delete the uploaded file if caching failed (optional but good cleanup)
                 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}")

                 return jsonify({
                     'success': False,
                     'error': 'PDF content is too small for caching. Please upload a larger document. Minimum token count varies by model, but is typically 1024+.',
                     'suggestion': 'Try uploading a longer document or combine multiple documents.'
                 })
            else:
                # Attempt to delete the uploaded file if caching failed
                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}")
                raise cache_error # Re-raise other errors

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

@app.route('/upload-url', methods=['POST'])
def upload_from_url():
    try:
        data = request.get_json()
        url = data.get('url')

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

        # Download file from URL
        try:
            response = httpx.get(url)
            response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
        except httpx.HTTPStatusError as e:
             return jsonify({'success': False, 'error': f'HTTP error downloading file from URL: {e.response.status_code} - {e.response.text}'})
        except httpx.RequestError as e:
             return jsonify({'success': False, 'error': f'Error downloading file from URL: {e}'})


        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
        try:
            document = client.upload_file(
                file=('downloaded_document.pdf', file_io, 'application/pdf'), # Use a placeholder filename
                display_name=url # Use the URL as display name
            )
            print(f"File uploaded successfully: {document.name}") # Log
        except Exception as upload_error:
             return jsonify({'success': False, 'error': f'Error uploading file to Gemini API: {str(upload_error)}'})
        # --- END CORRECTED FILE UPLOAD CALL ---


        # Create cache with system instruction
        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
                    system_instruction=system_instruction,
                    contents=[document], # document is the File object returned by upload_file
                    ttl="3600s",  # 1 hour TTL
                )
            )
            print(f"Cache created successfully: {cache.name}") # Log

            # Store cache info
            cache_id = str(uuid.uuid4())
            document_caches[cache_id] = {
                'cache_name': cache.name,
                'document_name': url,
                'created_at': datetime.now().isoformat()
            }

            # 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')


            return jsonify({
                'success': True,
                'cache_id': cache_id,
                'token_count': token_count
            })

        except Exception as cache_error:
            print(f"Cache creation failed: {str(cache_error)}") # Log the cache error
            # If caching fails due to small content, provide alternative approach
            if "Cached content is too small" in str(cache_error) or "minimum" in str(cache_error).lower():
                # Attempt to delete the uploaded file if caching failed (optional but good cleanup)
                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}")

                return jsonify({
                    'success': False,
                    'error': 'PDF content is too small for caching. Please upload a larger document. Minimum token count varies by model, but is typically 1024+.',
                    'suggestion': 'Try uploading a longer document or combine multiple documents.'
                })
            else:
                # Attempt to delete the uploaded file if caching failed
                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}")
                raise cache_error # Re-raise other errors


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

# ... (ask_question, list_caches, delete_cache routes remain largely the same) ...

@app.route('/ask', methods=['POST'])
def ask_question():
    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'})

        if cache_id not in document_caches:
            # Check if the cache still exists in Gemini API if it's not in our local map
            # This adds robustness if the server restarts or cache expires
            try:
                cache_info_api = client.caches.get(name=document_caches[cache_id]['cache_name']) # Need cache_name from stored info
                # If get succeeds, update local cache (or handle this differently)
                # For simplicity here, let's just fail if not in local map as it's in-memory
                 return jsonify({'success': False, 'error': 'Cache not found or expired. Please upload the document again.'})
            except Exception as get_error:
                 # If get fails, it's definitely gone
                 if cache_id in document_caches: # Clean up local entry if API confirms deletion/expiry
                     del document_caches[cache_id]
                 return jsonify({'success': False, 'error': 'Cache not found or expired. Please upload the document again.'})


        cache_info = document_caches[cache_id]

        # Generate response using cached content with correct model format
        response = client.models.generate_content(
            model='models/gemini-2.0-flash-001',
            contents=question, # User's question
            generation_config=types.GenerateContentConfig( # generation_config takes GenerateContentConfig
                cached_content=cache_info['cache_name']
            )
        )

        # Check if response has parts before accessing .text
        answer = "Could not generate response."
        if response and response.candidates and response.candidates[0].content and response.candidates[0].content.parts:
             answer = "".join(part.text for part in response.candidates[0].content.parts if hasattr(part, 'text'))
        elif response and response.prompt_feedback and response.prompt_feedback.block_reason:
             answer = f"Request blocked: {response.prompt_feedback.block_reason.name}"
        else:
             print(f"Unexpected response structure: {response}") # Log unexpected structure


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

    except Exception as e:
        print(f"An error occurred during question asking: {str(e)}") # Log errors
        return jsonify({'success': False, 'error': str(e)})

# ... (list_caches, delete_cache remain largely the same) ...

@app.route('/cache/<cache_id>', methods=['DELETE'])
def delete_cache(cache_id):
    try:
        if cache_id not in document_caches:
            return jsonify({'success': False, 'error': 'Cache not found'})

        cache_info = document_caches[cache_id]

        # Delete from Gemini API
        try:
            client.caches.delete(cache_info['cache_name'])
            print(f"Gemini cache deleted: {cache_info['cache_name']}") # Log
        except Exception as delete_error:
             print(f"Error deleting Gemini cache {cache_info['cache_name']}: {delete_error}") # Log
             # Decide if you want to fail if API deletion fails or just remove local entry
             # For robustness, maybe log and still remove local entry? Or return error?
             # Let's return an error for now.
             return jsonify({'success': False, 'error': f'Failed to delete cache from API: {str(delete_error)}'})

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

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

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


if __name__ == '__main__':
    import os
    # Ensure GOOGLE_API_KEY is set
    if not os.getenv('GOOGLE_API_KEY'):
        print("Error: GOOGLE_API_KEY environment variable not set.")
        # exit(1) # Or handle appropriately
        # For local testing with debug=True, you might pass it directly or ensure your .env is loaded
        pass # Allow running without key for now if needed, but API calls will fail

    port = int(os.environ.get("PORT", 7860))
    print(f"Starting Flask app on port {port}") # Log start
    # In production, set debug=False
    app.run(debug=True, host='0.0.0.0', port=port)