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@@ -98,17 +98,24 @@ def register_user(username, password, email):
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return False
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def send_email(to_email, subject, body, to_name="User"):
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- try:
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- msg = EmailMessage()
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- msg.set_content(body)
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- msg['Subject'] = subject
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- msg['From'] = '"Clinical Food AI System" <security@localfoodai.com>'
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- msg['To'] = f'"{to_name}" <{to_email}>'
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- s = smtplib.SMTP('localhost', 25)
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- s.send_message(msg)
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- s.quit()
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- except Exception:
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- print(f"Mock SMTP -> Sent to {to_email} | Subject: {subject}")
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+ msg = EmailMessage()
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+ msg.set_content(body)
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+ msg['Subject'] = subject
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+ msg['From'] = '"Clinical Food AI System" <security@localfoodai.com>'
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+ msg['To'] = f'"{to_name}" <{to_email}>'
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+
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+ import time
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+ for attempt in range(5):
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+ try:
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+ s = smtplib.SMTP('localhost', 25)
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+ s.send_message(msg)
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+ s.quit()
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+ return True
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+ except Exception as e:
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+ if attempt == 4:
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+ return f"SMTP Delivery Failed: {str(e)}"
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+ time.sleep(2)
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+ return "Unknown Error Occurred"
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def reset_password(username, email):
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conn = get_db_connection('app_auth')
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@@ -122,8 +129,10 @@ def reset_password(username, email):
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cursor.execute("UPDATE users SET password_hash = %s WHERE id = %s", (hashed, user['id']))
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conn.commit()
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conn.close()
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- send_email(email, "Password Reset", f"Your new temporary password is: {new_pass}", to_name=username.title())
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- return True
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+ status = send_email(email, "Password Reset", f"Your new temporary password is: {new_pass}", to_name=username.title())
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+ if status is True:
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+ return True
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+ return status
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return False
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# UI Theming
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@@ -211,8 +220,11 @@ with st.sidebar:
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f_user = st.text_input("Username", key="f_user")
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f_email = st.text_input("Registered Email", key="f_email")
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if st.button("Send Reset Link"):
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- if reset_password(f_user, f_email): st.success("Password reset emailed.")
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- else: st.error("Failed.")
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+ status = reset_password(f_user, f_email)
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+ if status is True:
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+ st.success("Password reset emailed.")
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+ else:
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+ st.error(f"Failed: {status}")
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if not st.session_state["authenticated_user"]:
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st.title("🍔 Food AI Medical Explorer")
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@@ -287,9 +299,7 @@ with tab_explore:
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with conn_reader.cursor() as cursor:
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l_str = "" if limit_rc == "All" else f"LIMIT {limit_rc}"
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query = f"""
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- SELECT code, product_name, generic_name, brands, allergens, ingredients_text,
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- proteins_100g, fat_100g, carbohydrates_100g, sugars_100g, sodium_100g, energy_kcal_100g,
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- `vitamin-c_100g`, iron_100g, calcium_100g
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+ SELECT *
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FROM products
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WHERE MATCH(product_name, ingredients_text) AGAINST(%s IN NATURAL LANGUAGE MODE)
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AND (proteins_100g >= %s OR proteins_100g IS NULL)
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@@ -305,6 +315,25 @@ with tab_explore:
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# Fetch EAV Medical Profile
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eav_profile = get_eav_profile(st.session_state["authenticated_user"])
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df = pd.DataFrame(results)
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+
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+ st.markdown("### 🛠️ Dynamic Column Display")
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+ default_columns = [
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+ 'code', 'product_name', 'generic_name', 'brands', 'allergens', 'ingredients_text',
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+ 'proteins_100g', 'fat_100g', 'carbohydrates_100g', 'sugars_100g', 'sodium_100g', 'energy-kcal_100g',
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+ 'vitamin-c_100g', 'iron_100g', 'calcium_100g'
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+ ]
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+ all_fetched_cols = list(df.columns)
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+ valid_defaults = [c for c in default_columns if c in all_fetched_cols]
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+
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+ if "selected_columns" not in st.session_state or st.button("Reset Default Columns"):
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+ st.session_state["selected_columns"] = valid_defaults
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+ st.rerun()
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+
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+ chosen_cols = st.multiselect("Customize Dataset View", all_fetched_cols, default=st.session_state["selected_columns"], key="multi_cols")
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+ st.session_state["selected_columns"] = chosen_cols
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+
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+ # Filter dataframe gracefully, but we retain a copy for background analytics
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+ df_display = df[chosen_cols].copy()
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warnings_col = []
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for idx, row in df.iterrows():
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@@ -339,27 +368,49 @@ with tab_explore:
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if val == 'osteoporosis' and pd.notnull(row.get('calcium_100g')) and float(row['calcium_100g']) > 0.1:
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warns.append("💚 High Calcium (Bone Health)")
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- # Dietary Analytics (Best-Effort Keyword Filters)
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- if cat == 'diet':
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- if val in ['vegan', 'kosher', 'halal']:
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- if val not in ing_text:
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- warns.append(f"⚠️ Cannot verify {val.title()} compliance. Please check manual label.")
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- if val == 'vegan' and ('lait' in ing_text or 'milk' in ing_text or 'oeuf' in ing_text or 'egg' in ing_text or 'meat' in ing_text or 'viande' in ing_text):
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- warns.append("⚠️ Contains Animal Products (Not Vegan)")
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- if val == 'halal' and ('porc' in ing_text or 'gelatin' in ing_text or 'vin' in ing_text or 'wine' in ing_text):
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+ if eav_data:
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+ ing_text = str(row.get('ingredients_text', '')).lower()
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+ all_text = str(row.get('allergens', '')).lower()
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+ for e in eav_data:
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+ cat = str(e['name']).lower()
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+ val = str(e['value']).lower()
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+
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+ # Clinical Trace Checks...
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+ if cat == 'condition' and val == 'pregnant':
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+ if float(row.get('iron_100g', 0) or 0) < 0.003:
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+ warns.append("⚠️ Low Iron (Pregnancy Risk)")
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+ else:
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+ warns.append("💚 Recommended (High Iron)")
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+
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+ if cat == 'illness' and val == 'osteoporosis':
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+ if float(row.get('calcium_100g', 0) or 0) < 0.120:
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+ warns.append("⚠️ Low Calcium (Osteoporosis Risk)")
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+ else:
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+ warns.append("💚 Recommended (High Calcium)")
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+
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+ if cat == 'illness' and val == 'scurvy':
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+ if float(row.get('vitamin-c_100g', 0) or 0) < 0.010:
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+ warns.append("⚠️ Low Vitamin C (Scurvy Risk)")
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+ else:
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+ warns.append("💚 Recommended (High Vitamin C)")
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+
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+ if cat == 'diet' and val in ['vegan', 'vegetarian']:
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+ if any(x in ing_text for x in ['meat', 'beef', 'chicken', 'fish', 'gelatin', 'whey']):
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+ warns.append("⚠️ Contains Animal Products")
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+ if cat == 'diet' and val == 'halal':
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+ if any(x in ing_text for x in ['pork', 'pig', 'wine', 'alcohol', 'beer']):
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warns.append("⚠️ Probable Haram Ingredients (e.g. Pork/Wine)")
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- # Simple Exclusion List Analytics
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- if cat in ['dislike', 'allergy']:
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- if val in ing_text or val in all_text:
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- warns.append(f"⚠️ Contains: {val.upper()}")
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-
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+ if cat in ['dislike', 'allergy']:
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+ if val in ing_text or val in all_text:
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+ warns.append(f"⚠️ Contains: {val.upper()}")
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+
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warnings_col.append(" | ".join(list(set(warns))) if warns else "✅ Safe for Profile")
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- df.insert(0, 'Medical Warning', warnings_col)
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- styled_df = df.style.apply(highlight_medical_warnings, axis=1)
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+ df_display.insert(0, 'Medical Warning', warnings_col)
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+ styled_df = df_display.style.apply(highlight_medical_warnings, axis=1)
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- st.success(f"Analysed {len(results)} records utilizing dynamic EAV parameters.")
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+ st.success(f"Analysed {len(results)} records utilizing dynamic Partitions!")
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st.dataframe(styled_df, use_container_width=True)
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else:
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st.warning("No products found matching those strict terms.")
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@@ -416,7 +467,14 @@ with tab_planner:
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if st.button("Generate Professional Menu"):
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with st.spinner("AI is formulating..."):
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- sys_prompt = f"Dietitian planner. {diet_pref}, {target_cal}kcal, {meal_count} meals. Notes: {extra_notes}. OUTPUT AS STRICT MARKDOWN TABLE."
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+ sys_prompt = f"""You are a professional Dietitian planner. Target: {target_cal}kcal over {meal_count} meals.
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+ Dietary constraint: {diet_pref}. Additional notes: {extra_notes}.
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+ CRITICAL INSTRUCTIONS:
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+ - ALWAYS output exactly as a strict Markdown table including Columns: | Meal | Food | Calories | Salt | Fat | Iron |
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+ - DO NOT output | separated text outside of standard strict markdown block ` ```markdown ` or standard rendering.
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+ - Convert ALL cooking measurements to Grams (g). Use these equivalents STRICTLY:
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+ 1 tbsp = 15g, 1 tsp = 5g, 1 cup = 200g, 1 mustard glass = 100g. 1 cl of liquid = 10g.
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+ """
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response = ollama.chat(model='mistral', messages=[{'role': 'system', 'content': sys_prompt}, {'role': 'user', 'content': 'Generate menu'}])
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st.markdown(response['message']['content'])
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lanfr144