Rename routes/query_handler.py to routes/input_handler.py

routes/{query_handler.py → input_handler.py}

@@ -1,8 +1,8 @@
 from fastapi import APIRouter, status, HTTPException
 from models.query import Query
 from routes import search_products, purchase, order_management, account_management, customer_support
-from services.nlp import recognize_intent, recognize_entities, extract_keywords, generate_response
-from services.utils import clean_text, encode_and_normalize, extract_order_id_from_query
+from services.nlp import recognize_intent, generate_response, recognize_speech
+from services.utils import extract_order_id_from_query, generate_image_embedding, generate_text_embedding
 
 
 router = APIRouter()
@@ -23,28 +23,47 @@ FUNCTION_DESCRIPTIONS_FOR_ORDERS = {
     "cancel_order": "Process order cancellation requests"
 }
 
+image_extensions = (".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff")
+audio_extensions = (".mp3", ".wav", ".flac", ".ogg", ".aac", ".m4a")
 
-def query_processing(query: Query):
-    cleaned_text = clean_text(query.text)
-    query.intent = recognize_intent(cleaned_text)
-    query.entities = recognize_entities(cleaned_text)
-    query.keywords = extract_keywords(cleaned_text)
-    encoded_query = encode_and_normalize(cleaned_text)
 
-    if query.intent == "search for products":
-        return {"products": search_products.handle_search_products_by_keywords(encoded_query)}
+def query_processing(input: Input):
 
-    elif query.intent == "order management":
-        order_id = extract_order_id_from_query(query.text)
+    if input.text:
+        intent_from_text = recognize_intent(input.text)
+        encoded_text = generate_text_embedding(input.text)
+
+        print(f'Intent_from_text: {intent_from_text}')
+        print(f'Text embedding: {encoded_text.shape}')
+
+    if input.files:
+        for file in input.files:
+            if file.endswith(audio_extensions):
+                text_from_audio = recognize_speech(file)
+                print(f'Transcription: {text_from_audio}')
+                # history_openai_format.append({"role": "user", "content": message})
+            elif file.endswith(image_extensions):
+                image_vector = generate_image_embedding(file)
+                print(f'Shape of the image vector: {image_vector.shape}')
+                return {"Similar products to the image attached": search_products.handle_search_products_by_keywords(image_vector)}
+                # history_openai_format.append({"role": "user", "content": "User has sent an image."})
+            else:
+                return "Please attach a valid file (image or audio)"
+
+    if intent_from_text == "search for products":
+        return {"products related to the search": search_products.handle_search_products_by_keywords(encoded_text)}
+
+    elif intent_from_text == "order management":
+        order_id = extract_order_id_from_query(input.text)
         if order_id:
             return order_management.handle_track_order(order_id)
         else:
-            return "Please provide an Order Number"
+            return "Please explain to the user that an order number should be provided in the chat interface"
     else:
         return None
 
 
 @router.post("/")
-async def handle_response(query: Query):
-    context_from_elasticsearch = query_processing(query)
-    return {"generative response": generate_response(query, context_from_elasticsearch)}
+async def handle_input(input: Input):
+    context_from_elasticsearch = query_processing(input)
+    return {"generative response": generate_response(input, context_from_elasticsearch)}