Update app.py

app.py

@@ -767,19 +767,30 @@ async def recipes_endpoint(profile: MedicalProfile):
         file_path = 'recipes_metadata.xlsx'
         metadata_path = 'recipes_metadata.xlsx'
         metadata_df = pd.read_excel(file_path)
-
-        # Prepare the final recipes list
-        recipes = []
-        # Combine scores with resources
-        for i, recipe in enumerate(recipes):
-            recipe["score"] = scores[i] if i < len(scores) else 0.0
-
-        # Sort resources by score
-        recipes.sort(key=lambda x: x["score"], reverse=True)
-
-
-        # Limit the response to top 5 recipes
-        return {"recipes": recipes[:5], "success": True}
+        relevant_portions = extract_relevant_portions(document_texts, query_text, max_portions=3, portion_size=1, min_query_words=1)
+        flattened_relevant_portions = []
+        for doc_id, portions in relevant_portions.items():
+            flattened_relevant_portions.extend(portions)
+        unique_selected_parts = remove_duplicates(flattened_relevant_portions)
+        combined_parts = " ".join(unique_selected_parts)
+        context = [query_text] + unique_selected_parts
+        entities = extract_entities(query_text)
+        passage = enhance_passage_with_entities(combined_parts, entities)
+        prompt = create_prompt(query_text, passage)
+        answer = generate_answer(prompt)
+        answer_part = answer.split("Answer:")[-1].strip()
+        cleaned_answer = remove_answer_prefix(answer_part)
+        final_answer = remove_incomplete_sentence(cleaned_answer)
+        if language_code == 0:
+            final_answer = translate_en_to_ar(final_answer)
+        if final_answer:
+            print("Answer:")
+            print(final_answer)
+        else:
+            print("Sorry, I can't help with that.")
+        return {
+            "response": final_answer,
+        }
 
     except ValueError as ve:
         # Handle expected errors