Created app.py

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from google import genai
+from PIL import Image
+
+# Initialize Gemini Client
+client = genai.Client()
+
+# Force full viewport usage to widen the camera layout frame
+st.set_page_config(page_title="NutriScan AI Pro", page_icon="🛡️", layout="wide")
+
+st.title("🛡️ NutriScan AI: Conversational Diet Intelligence")
+st.write("A production-hardened hybrid edge/cloud system with dynamic session memory.")
+
+st.markdown("---")
+
+# --- STEP 1: CONTEXT INJECTION DECK (SIDEBAR) ---
+st.sidebar.header("👤 Dynamic Health Profile")
+
+goal_options = ["Weight Loss", "Muscle Gain / Clean Bulk", "Diabetes Management", "Hypertension Control", "Gluten-Free Induction"]
+selected_goals = st.sidebar.multiselect("Primary Objectives", options=goal_options)
+
+# Custom Goal Input Gate
+custom_goal_active = st.sidebar.checkbox("Inject custom targets?")
+custom_goal_text = ""
+if custom_goal_active:
+    custom_goal_text = st.sidebar.text_input("Type custom health profile constraints:")
+
+allergy_options = ["Dairy", "Nuts", "Gluten", "Soy", "Artificial Sweeteners", "Preservatives"]
+selected_allergies = st.sidebar.multiselect("STRICT Avoidances / Allergies", options=allergy_options)
+
+additional_notes = st.sidebar.text_area("Narrative Clinical Notes (e.g., medical conditions):")
+
+# Construct the master string block
+final_goals = selected_goals + ([custom_goal_text] if custom_goal_text else [])
+user_profile_context = f"""
+USER HEALTH PROFILE DOSSIER:
+- Objectives: {', '.join(final_goals) if final_goals else 'General Fitness Check'}
+- Strict Allergens to Flag: {', '.join(selected_allergies) if selected_allergies else 'None specified'}
+- Medical/Narrative Notes: {additional_notes if additional_notes else 'None provided'}
+"""
+
+# --- STEP 2: CONVERSATIONAL MEMORY INITIALIZATION ---
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+if "vault_images" not in st.session_state:
+    st.session_state.vault_images = []
+
+# --- STEP 3: MULTI-IMAGE ACQUISITION CANVAS ---
+st.subheader("📸 Frame Capture Pipeline")
+col_cam, col_vault = st.columns([2, 3])
+
+with col_cam:
+    captured_file = st.camera_input("Position product packaging in center view")
+    if captured_file is not None:
+        img = Image.open(captured_file)
+        
+        # Guard against duplicates inside the active frame cycle
+        if len(st.session_state.vault_images) == 0 or captured_file.name != st.session_state.get("last_uploaded_name", ""):
+            st.session_state.vault_images.append(img)
+            st.session_state.last_uploaded_name = captured_file.name
+            st.success(f"Frame buffered into system memory! Canvas Count: {len(st.session_state.vault_images)}")
+
+with col_vault:
+    if st.session_state.vault_images:
+        st.write("⚡ **Buffered Frame Stack Active:**")
+        # Render thumbnails of all taken photos side-by-side
+        thumb_cols = st.columns(min(len(st.session_state.vault_images), 4))
+        for idx, thumb_img in enumerate(st.session_state.vault_images):
+            with thumb_cols[idx % 4]:
+                st.image(thumb_img, caption=f"Scan #{idx+1}", width=120)
+        
+        if st.button("🗑️ Clear Image Stack"):
+            st.session_state.vault_images = []
+            st.rerun()
+
+st.markdown("---")
+
+# --- STEP 4: INTERACTIVE CHAT ENGINE LAYOUT ---
+st.subheader("💬 AI Clinical Consultation Stream")
+
+# Render previous conversational statements
+for message in st.session_state.chat_history:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# System Execution Prompter
+if user_message := st.chat_input("Ask a question about your scanned items..."):
+    
+    # 1. Display User Message Instantly
+    st.session_state.chat_history.append({"role": "user", "content": user_message})
+    with st.chat_message("user"):
+        st.markdown(user_message)
+        
+    # 2. Build Multi-modal Prompt Strategy
+    # Construct systemic ground truth logic framework
+    system_logic_prompt = f"""
+    You are an expert digital dietitian. You are analyzing an interactive product scan loop.
+    
+    CRITICAL OPERATION PROTOCOLS:
+    1. Cross-reference all inputs against this profile context: {user_profile_context}
+    2. Analyze the attached sequence of product photos sequentially. 
+    3. If the user's query requires finer granular data that you cannot see in the current image stack, or if a photo is unclear, DO NOT guess. State your initial observation and explicitly request the user to take an additional focused scan.
+    
+    CURRENT CHAT HISTORY DIALOGUE FOR TRACKING CONTEXT:
+    """
+    
+    # Pack background context strings, active image arrays, and current input together
+    payload = [system_logic_prompt]
+    
+    # Compile chat history text strings into payload
+    for msg in st.session_state.chat_history[:-1]:
+        payload.append(f"{msg['role'].upper()}: {msg['content']}\n")
+        
+    # Inject our list of images directly into the multimodal generation array
+    payload.extend(st.session_state.vault_images)
+    
+    # Inject current fresh query prompt
+    payload.append(f"CURRENT USER INQUIRY: {user_message}\nASSISTANT SYSTEM OUTPUT:")
+    
+    # 3. Call Cloud Model Infrastructure
+    with st.chat_message("assistant"):
+        with st.spinner("Analyzing data streams..."):
+            try:
+                response = client.models.generate_content(
+                    model='gemini-2.5-flash',
+                    contents=payload
+                )
+                st.markdown(response.text)
+                st.session_state.chat_history.append({"role": "assistant", "content": response.text})
+            except Exception as e:
+                st.error(f"Execution Error: {e}")