Upd summariser

memory/memory.py

@@ -7,7 +7,7 @@ import faiss
 from sentence_transformers import SentenceTransformer
 from google import genai  # must be configured in app.py and imported globally
 import logging
-from models.summarizer import get_summarizer
+from models.summarizer import summarizer
 
 _LLM_SMALL = "gemini-2.5-flash-lite-preview-06-17"
 # Load embedding model

models/summarizer.py

@@ -39,21 +39,21 @@ class TextSummarizer:
         return text.strip()
     
     def extract_key_phrases(self, text: str) -> List[str]:
-        """Extract key medical phrases and terms"""
+        """Extract key cooking phrases and terms"""
         if not text:
             return []
         
-        # Medical term patterns
-        medical_patterns = [
-            r'\b(?:symptoms?|diagnosis|treatment|therapy|medication|drug|disease|condition|syndrome)\b',
-            r'\b(?:patient|doctor|physician|medical|clinical|healthcare)\b',
-            r'\b(?:blood pressure|heart rate|temperature|pulse|respiration)\b',
-            r'\b(?:acute|chronic|severe|mild|moderate|serious|critical)\b',
-            r'\b(?:pain|ache|discomfort|swelling|inflammation|infection)\b'
+        # Cooking term patterns
+        cooking_patterns = [
+            r'\b(?:recipe|ingredients?|cooking|baking|roasting|grilling|frying|boiling|steaming)\b',
+            r'\b(?:chef|cook|kitchen|cuisine|meal|dish|food|taste|flavor)\b',
+            r'\b(?:temperature|timing|preparation|technique|method|seasoning|spices?|herbs?)\b',
+            r'\b(?:oven|stovetop|grill|pan|pot|skillet|knife|cutting|chopping)\b',
+            r'\b(?:sauce|marinade|dressing|garnish|presentation|serving)\b'
         ]
         
         key_phrases = []
-        for pattern in medical_patterns:
+        for pattern in cooking_patterns:
             matches = re.findall(pattern, text, re.IGNORECASE)
             key_phrases.extend(matches)
         
@@ -70,10 +70,10 @@ class TextSummarizer:
             
             # Extract key phrases for context
             key_phrases = self.extract_key_phrases(cleaned_text)
-            key_phrases_str = ", ".join(key_phrases[:5]) if key_phrases else "medical information"
+            key_phrases_str = ", ".join(key_phrases[:5]) if key_phrases else "cooking information"
             
             # Create optimized prompt
-            prompt = f"""Summarize this medical text in {max_length} characters or less. Focus only on key medical facts, symptoms, treatments, and diagnoses. Do not include greetings, confirmations, or conversational elements.
+            prompt = f"""Summarize this cooking text in {max_length} characters or less. Focus only on key cooking facts, recipes, techniques, and ingredients. Do not include greetings, confirmations, or conversational elements.
 
 Key terms: {key_phrases_str}
 
@@ -110,7 +110,7 @@ Summary:"""
 
             # Short, strict prompt to avoid verbosity; instruct to output NOTHING if irrelevant
             prompt = (
-                f"You extract only medically relevant facts that help answer: '{query}'. "
+                f"You extract only cooking relevant facts that help answer: '{query}'. "
                 f"Respond with a concise bullet list (<= {max_length} chars total). "
                 "If the content is irrelevant, respond with EXACTLY: NONE.\n\n"
                 f"Content: {cleaned_text[:1600]}\n\nRelevant facts:"
@@ -138,12 +138,12 @@ Summary:"""
                 url_mapping[doc_id] = doc['url']
                 
                 # Create focused summary for each document
-                summary_prompt = f"""Summarize this medical document in 2-3 sentences, focusing on information relevant to: "{user_query}"
+                summary_prompt = f"""Summarize this cooking document in 2-3 sentences, focusing on information relevant to: "{user_query}"
 
 Document: {doc['title']}
 Content: {doc['content'][:800]}
 
-Key medical information:"""
+Key cooking information:"""
 
                 summary = self.llama_client._call_llama(summary_prompt)
                 summary = self.clean_text(summary)
@@ -165,11 +165,11 @@ Key medical information:"""
             
             cleaned_chunk = self.clean_text(chunk)
             
-            prompt = f"""Summarize this medical conversation in 1-2 sentences. Focus only on medical facts, symptoms, treatments, or diagnoses discussed. Remove greetings and conversational elements.
+            prompt = f"""Summarize this cooking conversation in 1-2 sentences. Focus only on cooking facts, recipes, techniques, or ingredients discussed. Remove greetings and conversational elements.
 
 Conversation: {cleaned_chunk[:1000]}
 
-Medical summary:"""
+Cooking summary:"""
 
             summary = self.llama_client._call_llama(prompt)
             return self.clean_text(summary)

utils/vlm.py

@@ -1,54 +0,0 @@
-import os, logging, traceback, json, base64
-from io import BytesIO
-from PIL import Image
-from .translation import translate_query
-from gradio_client import Client, handle_file
-import tempfile
-
-logger = logging.getLogger("vlm-agent")
-logging.basicConfig(level=logging.INFO, format="%(asctime)s — %(name)s — %(levelname)s — %(message)s", force=True)
-
-# ✅ Load Gradio client once
-gr_client = None
-def load_gradio_client():
-    global gr_client
-    if gr_client is None:
-        logger.info("[VLM] ⏳ Connecting to MedGEMMA Gradio Space...")
-        gr_client = Client("warshanks/medgemma-4b-it")
-        logger.info("[VLM] Gradio MedGEMMA client ready.")
-    return gr_client
-
-def process_medical_image(base64_image: str, prompt: str = None, lang: str = "EN") -> str:
-    if not prompt:
-        prompt = "Describe and investigate any clinical findings from this medical image."
-    elif lang.upper() in {"VI", "ZH"}:
-        prompt = translate_query(prompt, lang.lower())
-
-    try:
-        # 1️⃣ Decode base64 image to temp file
-        image_data = base64.b64decode(base64_image)
-        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
-            tmp.write(image_data)
-            tmp.flush()
-            image_path = tmp.name
-
-        # 2️⃣ Send to Gradio MedGEMMA
-        client = load_gradio_client()
-        logger.info(f"[VLM] Sending prompt: {prompt}")
-        result = client.predict(
-            message={"text": prompt, "files": [handle_file(image_path)]},
-            param_2 = "You analyze medical images and report abnormalities, diseases with clear diagnostic insight.",
-            param_3=2048,
-            api_name="/chat"
-        )
-        if isinstance(result, str):
-            logger.info(f"[VLM] ✅ Response: {result}")
-            return result.strip()
-        else:
-            logger.warning(f"[VLM] ⚠️ Unexpected result type: {type(result)} — {result}")
-            return str(result)
-
-    except Exception as e:
-        logger.error(f"[VLM] ❌ Exception: {e}")
-        logger.error(f"[VLM] 🔍 Traceback:\n{traceback.format_exc()}")
-        return f"[VLM] ⚠️ Failed to process image: {e}"