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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>
"""
@app.route('/')
def index():
return render_template_string(HTML_TEMPLATE)
@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)
# Upload to Gemini File API
document = client.files.upload(
file=file_io,
config=dict(mime_type='application/pdf')
)
# 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'
cache = client.caches.create(
model=model,
config=types.CreateCachedContentConfig(
display_name='pdf document cache',
system_instruction=system_instruction,
contents=[document],
ttl="3600s", # 1 hour TTL
)
)
# 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()
}
return jsonify({
'success': True,
'cache_id': cache_id,
'token_count': getattr(cache.usage_metadata, 'cached_token_count', 'Unknown')
})
except Exception as cache_error:
# If caching fails due to small content, provide alternative approach
if "Cached content is too small" in str(cache_error):
return jsonify({
'success': False,
'error': 'PDF is too small for caching. Please upload a larger document (minimum 4,096 tokens required).',
'suggestion': 'Try uploading a longer document or combine multiple documents.'
})
else:
raise cache_error
except Exception as e:
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
response = httpx.get(url)
response.raise_for_status()
file_io = io.BytesIO(response.content)
# Upload to Gemini File API
document = client.files.upload(
file=file_io,
config=dict(mime_type='application/pdf')
)
# 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'
cache = client.caches.create(
model=model,
config=types.CreateCachedContentConfig(
display_name='pdf document cache',
system_instruction=system_instruction,
contents=[document],
ttl="3600s", # 1 hour TTL
)
)
# Store cache info
cache_id = str(uuid.uuid4())
document_caches[cache_id] = {
'cache_name': cache.name,
'document_name': url,
'created_at': datetime.now().isoformat()
}
return jsonify({
'success': True,
'cache_id': cache_id,
'token_count': getattr(cache.usage_metadata, 'cached_token_count', 'Unknown')
})
except Exception as cache_error:
# If caching fails due to small content, provide alternative approach
if "Cached content is too small" in str(cache_error):
return jsonify({
'success': False,
'error': 'PDF is too small for caching. Please upload a larger document (minimum 4,096 tokens required).',
'suggestion': 'Try uploading a longer document or combine multiple documents.'
})
else:
raise cache_error
except Exception as e:
return jsonify({'success': False, 'error': str(e)})
@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:
return jsonify({'success': False, 'error': 'Cache not found'})
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,
config=types.GenerateContentConfig(
cached_content=cache_info['cache_name']
)
)
return jsonify({
'success': True,
'answer': response.text
})
except Exception as e:
return jsonify({'success': False, 'error': str(e)})
@app.route('/caches', methods=['GET'])
def list_caches():
try:
caches = []
for cache_id, cache_info in document_caches.items():
caches.append({
'cache_id': cache_id,
'document_name': cache_info['document_name'],
'created_at': cache_info['created_at']
})
return jsonify({'success': True, 'caches': caches})
except Exception as e:
return jsonify({'success': False, 'error': str(e)})
@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
client.caches.delete(cache_info['cache_name'])
# Remove from local storage
del document_caches[cache_id]
return jsonify({'success': True, 'message': 'Cache deleted successfully'})
except Exception as e:
return jsonify({'success': False, 'error': str(e)})
if __name__ == '__main__':
import os
port = int(os.environ.get("PORT", 7860))
app.run(debug=True, host='0.0.0.0', port=port) |