Upload app.py

app.py

@@ -4,6 +4,11 @@ import json
 import numpy as np
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
+import logging
+import time
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
 
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
@@ -11,27 +16,42 @@ For more information on `huggingface_hub` Inference API support, please check th
 
 # Load embeddings from a JSON file
 def load_embeddings(file_path):
+    logging.info(f"Loading embeddings from {file_path}")
     with open(file_path, 'r', encoding='utf-8') as file:
-        return json.load(file)
+        embeddings = json.load(file)
+        logging.info(f"Loaded {len(embeddings)} embeddings")
+        return embeddings
 
 # Function to get relevant articles based on user query
 def get_relevant_documents(query, embeddings_data, model, top_k=3):
+    logging.info(f"Received query: {query}")
+    start_time = time.time()
+
     query_embedding = model.encode(query)
     similarities = []
 
-    for entry in embeddings_data:
+    for i, entry in enumerate(embeddings_data):
         embedding = np.array(entry['embedding'])
         similarity = cosine_similarity([query_embedding], [embedding])[0][0]
         similarities.append((entry, similarity))
+        if i % 100 == 0:  # Log every 100 iterations
+            logging.debug(f"Processed {i} embeddings")
 
-    # Sort by similarity and return top_k relevant entries
+    logging.info("Sorting similarities")
     similarities.sort(key=lambda x: x[1], reverse=True)
     top_entries = [entry for entry, _ in similarities[:top_k]]
     
+    end_time = time.time()
+    duration = end_time - start_time
+    
+    logging.info(f"Query processed in {duration:.2f} seconds")
+    logging.info(f"Top {top_k} documents returned with similarities: {[sim[1] for sim in similarities[:top_k]]}")
+
     return top_entries
 
 # Function to format relevant documents into a string
 def format_documents(documents):
+    logging.info(f"Formatting {len(documents)} documents")
     formatted = ""
     for doc in documents:
         formatted += f"Relevant article: {doc['name']}\n{doc['content']}\n\n"
@@ -48,12 +68,21 @@ def respond(
     embeddings_data,
     model
 ):
+    logging.info(f"New user query: {message}")
+    
+    start_time = time.time()
+
     # Search for relevant documents based on user input
     relevant_docs = get_relevant_documents(message, embeddings_data, model)
     retrieved_context = format_documents(relevant_docs)
     
+    # Log the statistics about the retrieved documents
+    logging.info(f"Total documents retrieved: {len(relevant_docs)}")
+    logging.info(f"Documents: " + {[doc['name'] for doc in relevant_docs]})
+    
     # Add the retrieved context as part of the system message
     system_message_with_context = system_message + "\n\n" + "Relevant documents:\n" + retrieved_context
+    logging.info("System message updated with retrieved context")
 
     messages = [{"role": "system", "content": system_message_with_context}]
 
@@ -64,10 +93,12 @@ def respond(
             messages.append({"role": "assistant", "content": val[1]})
 
     messages.append({"role": "user", "content": message})
+    logging.info("Messages prepared for InferenceClient")
 
     response = ""
     client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
     
+    logging.info("Sending request to InferenceClient")
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -78,11 +109,17 @@ def respond(
         token = message.choices[0].delta.content
         response += token
         yield response
+    
+    end_time = time.time()
+    total_duration = end_time - start_time
+    logging.info(f"Response generated in {total_duration:.2f} seconds")
 
 # Load embeddings and model once at startup
 embeddings_file = 'Code Civil vectorised.json'
+logging.info("Starting application, loading embeddings and model")
 embeddings_data = load_embeddings(embeddings_file)
-embedding_model = SentenceTransformer('Lajavaness/bilingual-embedding-small', trust_remote_code=True)
+embedding_model = SentenceTransformer('Lajavaness/bilingual-embedding-small')
+logging.info("Model and embeddings loaded successfully")
 
 # Gradio interface
 demo = gr.ChatInterface(
@@ -104,4 +141,5 @@ demo = gr.ChatInterface(
 )
 
 if __name__ == "__main__":
+    logging.info("Launching Gradio app")
     demo.launch()