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Yhhxhfh commited on Sep 24, 2024

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a4863fb

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1 Parent(s): eab90e8

Update app.py

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Files changed (1) hide show

app.py +106 -26

app.py CHANGED Viewed

@@ -12,6 +12,10 @@ from transformers import AutoTokenizer, GPT2LMHeadModel, pipeline
 from loguru import logger
 from dotenv import load_dotenv
 from sklearn.metrics.pairwise import cosine_similarity
 sys.path.append('..')
@@ -28,6 +32,33 @@ redis_client = redis.Redis(host=redis_host, port=redis_port, password=redis_pass
 MAX_ITEMS_PER_TABLE = 10000
 def get_current_table_index():
     return int(redis_client.get("current_table_index") or 0)
@@ -57,6 +88,20 @@ def load_and_store_models(model_names):
         except Exception as e:
             logger.error(f"Error loading model {name}: {e}")
 app = FastAPI()
 app.add_middleware(
     CORSMiddleware,
@@ -89,6 +134,21 @@ async def index():
             .user-message, .bot-message {{ margin-bottom: 10px; padding: 8px 12px; border-radius: 8px; max-width: 70%; word-wrap: break-word; }}
             .user-message {{ background-color: #007bff; color: #fff; align-self: flex-end; }}
             .bot-message {{ background-color: #4CAF50; color: #fff; }}
         </style>
     </head>
     <body>
@@ -98,25 +158,53 @@ async def index():
                 <div class="chat-box" id="chat-box">
                     {chat_history_html}
                 </div>
-                <input type="text" class="chat-input" id="user-input" placeholder="Type your message...">
             </div>
         </div>
         <script>
             const userInput = document.getElementById('user-input');
             userInput.addEventListener('keyup', function(event) {{
                 if (event.key === 'Enter') {{
                     event.preventDefault();
                     sendMessage();
                 }}
             }});
             function sendMessage() {{
                 const userMessage = userInput.value.trim();
                 if (userMessage === '') return;
                 appendMessage('user', userMessage);
                 userInput.value = '';
                 fetch(`/autocomplete?q=` + encodeURIComponent(userMessage))
                 .then(response => response.json())
@@ -159,41 +247,26 @@ def calculate_similarity(base_text, candidate_texts):
     return similarities
 @app.get('/autocomplete')
-async def autocomplete(q: str = Query(..., title='query'), background_tasks: BackgroundTasks = BackgroundTasks()): # Corrección: Mover background_tasks al final
     global message_history
     message_history.append(('user', q))
-    background_tasks.add_task(generate_responses, q)
-    return {"status": "Processing request, please wait..."}
 @app.get('/get_response')
 async def get_response(q: str = Query(..., title='query')):
     response = redis_client.hget("responses", q)
     return {"response": response}
-def generate_responses(q):
-    generated_responses = []
-    try:
-        for model_name in redis_client.hkeys("models"):
-            try:
-                model_data = redis_client.hget("models", model_name)
-                model = GPT2LMHeadModel.from_pretrained(model_name)
-                tokenizer = AutoTokenizer.from_pretrained(model_name)
-                text_generation_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
-                generated_response = text_generation_pipeline(q, do_sample=True, max_length=50, num_return_sequences=5)
-                generated_responses.extend([response['generated_text'] for response in generated_response])
-            except Exception as e:
-                logger.error(f"Error generating response with model {model_name}: {e}")
-        if generated_responses:
-            similarities = calculate_similarity(q, generated_responses)
-            most_coherent_response = generated_responses[np.argmax(similarities)]
-            store_to_redis_table(q, "\n".join(generated_responses))
-            redis_client.hset("responses", q, most_coherent_response)
-        else:
-            logger.warning("No valid responses generated.")
-    except Exception as e:
-        logger.error(f"General error in autocomplete: {e}")
 if __name__ == '__main__':
     gpt2_models = [
@@ -210,6 +283,13 @@ if __name__ == '__main__':
         "Salesforce/codegen-350M-multi"
     ]
     load_and_store_models(gpt2_models + programming_models)
     uvicorn.run(app=app, host='0.0.0.0', port=int(os.getenv("PORT", 7860)))

 from loguru import logger
 from dotenv import load_dotenv
 from sklearn.metrics.pairwise import cosine_similarity
+from kaggle.api.kaggle_api_extended import KaggleApi
+# Importar la librería de spaces
+from huggingface_hub import spaces
 sys.path.append('..')
 MAX_ITEMS_PER_TABLE = 10000
+# Decorador para usar GPU en Spaces
+@spaces.GPU()
+def generate_responses_gpu(q):
+    generated_responses = []
+    try:
+        for model_name in redis_client.hkeys("models"):
+            try:
+                model_data = redis_client.hget("models", model_name)
+                model = GPT2LMHeadModel.from_pretrained(model_name)
+                tokenizer = AutoTokenizer.from_pretrained(model_name)
+                text_generation_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
+                generated_response = text_generation_pipeline(q, do_sample=True, max_length=50, num_return_sequences=5)
+                generated_responses.extend([response['generated_text'] for response in generated_response])
+            except Exception as e:
+                logger.error(f"Error generating response with model {model_name}: {e}")
+        if generated_responses:
+            similarities = calculate_similarity(q, generated_responses)
+            most_coherent_response = generated_responses[np.argmax(similarities)]
+            store_to_redis_table(q, "\n".join(generated_responses))
+            redis_client.hset("responses", q, most_coherent_response)
+        else:
+            logger.warning("No valid responses generated.")
+    except Exception as e:
+        logger.error(f"General error in autocomplete: {e}")
 def get_current_table_index():
     return int(redis_client.get("current_table_index") or 0)
         except Exception as e:
             logger.error(f"Error loading model {name}: {e}")
+def load_kaggle_datasets(dataset_names):
+    api = KaggleApi()
+    api.authenticate()
+    for dataset_name in dataset_names:
+        try:
+            api.dataset_download_files(dataset_name, path='./kaggle_datasets', unzip=True)
+            dataset = load_dataset('csv', data_files=[f'./kaggle_datasets/{dataset_name}/*.csv'])['train']
+            sample_data = dataset.to_pandas().head(10).to_json(orient='records')
+            store_to_redis_table(dataset_name, sample_data)
+            redis_client.hset("kaggle_datasets", dataset_name, sample_data)
+        except Exception as e:
+            logger.error(f"Error loading Kaggle dataset {dataset_name}: {e}")
 app = FastAPI()
 app.add_middleware(
     CORSMiddleware,
             .user-message, .bot-message {{ margin-bottom: 10px; padding: 8px 12px; border-radius: 8px; max-width: 70%; word-wrap: break-word; }}
             .user-message {{ background-color: #007bff; color: #fff; align-self: flex-end; }}
             .bot-message {{ background-color: #4CAF50; color: #fff; }}
+            #autocomplete-suggestions {{
+                position: absolute;
+                background-color: #fff;
+                border: 1px solid #ccc;
+                border-radius: 4px;
+                z-index: 10;
+                max-width: calc(100% - 40px);
+            }}
+            .suggestion {{
+                padding: 8px;
+                cursor: pointer;
+            }}
+            .suggestion:hover {{
+                background-color: #f0f0f0;
+            }}
         </style>
     </head>
     <body>
                 <div class="chat-box" id="chat-box">
                     {chat_history_html}
                 </div>
+                <input type="text" class="chat-input" id="user-input" placeholder="Type your message..." autocomplete="off">
+                <div id="autocomplete-suggestions"></div>
             </div>
         </div>
         <script>
             const userInput = document.getElementById('user-input');
+            const autocompleteSuggestions = document.getElementById('autocomplete-suggestions');
             userInput.addEventListener('keyup', function(event) {{
                 if (event.key === 'Enter') {{
                     event.preventDefault();
                     sendMessage();
+                }} else {{
+                    fetch(`/autocomplete?q=` + encodeURIComponent(userInput.value))
+                    .then(response => response.json())
+                    .then(data => {{
+                        displayAutocompleteSuggestions(data.suggestions);
+                    }})
+                    .catch(error => {{
+                        console.error('Error:', error);
+                    }});
                 }}
             }});
+            function displayAutocompleteSuggestions(suggestions) {{
+                autocompleteSuggestions.innerHTML = '';
+                if (suggestions.length > 0) {{
+                    suggestions.forEach(suggestion => {{
+                        const suggestionElement = document.createElement('div');
+                        suggestionElement.className = 'suggestion';
+                        suggestionElement.innerText = suggestion;
+                        suggestionElement.onclick = () => {{
+                            userInput.value = suggestion;
+                            autocompleteSuggestions.innerHTML = '';
+                        }};
+                        autocompleteSuggestions.appendChild(suggestionElement);
+                    }});
+                }}
+            }}
             function sendMessage() {{
                 const userMessage = userInput.value.trim();
                 if (userMessage === '') return;
                 appendMessage('user', userMessage);
                 userInput.value = '';
+                autocompleteSuggestions.innerHTML = '';
                 fetch(`/autocomplete?q=` + encodeURIComponent(userMessage))
                 .then(response => response.json())
     return similarities
 @app.get('/autocomplete')
+async def autocomplete(q: str = Query(..., title='query'), background_tasks: BackgroundTasks = BackgroundTasks()):
     global message_history
     message_history.append(('user', q))
+    suggestions = []
+    if q:
+        for key in redis_client.hkeys("responses"):
+            if q.lower() in key.lower():
+                suggestions.append(key)
+    # Lanzar la tarea en segundo plano utilizando la función decorada con @spaces.GPU()
+    background_tasks.add_task(generate_responses_gpu, q)
+    return {"status": "Processing request, please wait...", "suggestions": suggestions}
 @app.get('/get_response')
 async def get_response(q: str = Query(..., title='query')):
     response = redis_client.hget("responses", q)
     return {"response": response}
 if __name__ == '__main__':
     gpt2_models = [
         "Salesforce/codegen-350M-multi"
     ]
+    kaggle_datasets = [
+        "uciml/iris",
+        "arshid/iris-flower-dataset",
+        "heesoo37/120-years-of-olympic-history-athletes-and-results"
+    ]
     load_and_store_models(gpt2_models + programming_models)
+    load_kaggle_datasets(kaggle_datasets)
     uvicorn.run(app=app, host='0.0.0.0', port=int(os.getenv("PORT", 7860)))