Update app.py

app.py

@@ -610,115 +610,6 @@ def remove_incomplete_sentence(text):
     return text
 
 
-import traceback
-
-language_code = 1
-query_text = "recipes and meals for vegan diabetes headache fatigue"
-print(f"Generated query text: {query_text}")
-
-try:
-    # Generate the query embedding
-    print("Generating query embedding...")
-    query_embedding = embed_query_text(query_text)
-    if query_embedding is None:
-        raise ValueError("Failed to generate query embedding.")
-    print(f"Query embedding generated: {query_embedding}")
-
-    # Load embeddings and retrieve initial results
-    print("Loading recipe embeddings...")
-    embeddings_data = load_recipes_embeddings()
-    print("Embeddings loaded. Retrieving initial results...")
-    initial_results = query_recipes_embeddings(query_embedding, embeddings_data, n_results=10)
-    if not initial_results:
-        raise ValueError("No relevant recipes found.")
-    print(f"Initial results: {initial_results}")
-
-    # Extract document IDs
-    document_ids = [doc_id for doc_id, _ in initial_results]
-    print(f"Document IDs: {document_ids}")
-
-    # Retrieve document texts
-    folder_path = 'downloaded_articles/downloaded_articles'
-    print("Retrieving document texts...")
-    document_texts = retrieve_rec_texts(document_ids, folder_path)
-    if not document_texts:
-        raise ValueError("Failed to retrieve document texts.")
-    print(f"Document texts retrieved: {document_texts}")
-
-    # Load recipe metadata from DataFrame
-    file_path = 'recipes_metadata.xlsx'
-    print("Loading metadata from Excel...")
-    metadata_df = pd.read_excel(file_path)
-    print(f"Metadata loaded: {metadata_df.head()}")
-
-    # Extract relevant portions
-    print("Extracting relevant portions...")
-    relevant_portions = extract_relevant_portions(
-        document_texts, query_text, max_portions=3, portion_size=1, min_query_words=1
-    )
-    print(f"Relevant portions: {relevant_portions}")
-
-    # Flatten portions
-    print("Flattening relevant portions...")
-    flattened_relevant_portions = []
-    for doc_id, portions in relevant_portions.items():
-        flattened_relevant_portions.extend(portions)
-    unique_selected_parts = remove_duplicates(flattened_relevant_portions)
-    print(f"Unique selected parts: {unique_selected_parts}")
-
-    # Combine parts into a single context
-    combined_parts = " ".join(unique_selected_parts)
-    print(f"Combined parts: {combined_parts}")
-    context = [query_text] + unique_selected_parts
-    print(f"Context: {context}")
-
-    # Extract entities
-    print("Extracting entities...")
-    entities = extract_entities(query_text)
-    print(f"Entities: {entities}")
-
-    # Enhance passage with entities
-    print("Enhancing passage with entities...")
-    passage = enhance_passage_with_entities(combined_parts, entities)
-    print(f"Enhanced passage: {passage}")
-
-    # Create the prompt
-    print("Creating prompt...")
-    prompt = create_prompt(query_text, passage)
-    print(f"Prompt: {prompt}")
-
-    # Generate the answer
-    print("Generating answer...")
-    answer = generate_answer(prompt)
-    print(f"Answer: {answer}")
-    answer_part = answer.split("Answer:")[-1].strip()
-    print(f"Answer part: {answer_part}")
-
-    # Clean and finalize the answer
-    print("Cleaning answer...")
-    cleaned_answer = remove_answer_prefix(answer_part)
-    print(f"Cleaned answer: {cleaned_answer}")
-    final_answer = remove_incomplete_sentence(cleaned_answer)
-    print(f"Final answer: {final_answer}")
-
-    # Translate if needed
-    if language_code == 0:
-        print("Translating answer to Arabic...")
-        final_answer = translate_en_to_ar(final_answer)
-
-    # Display the answer
-    if final_answer:
-        print("Final Answer:")
-        print(final_answer)
-    else:
-        print("Sorry, I can't help with that.")
-except Exception as e:
-    print("An error occurred:")
-    print(traceback.format_exc())
-
-
-
-
 @app.get("/")
 async def root():
     return {"message": "Welcome to the FastAPI application! Use the /health endpoint to check health, and /api/query for processing queries."}