Update app.py

app.py

@@ -1,408 +1,408 @@
-# =============================================================================
-# 🥗 NutriVision - app.py
-# Vision Models: nateraw/food | prithivMLmods/Indian-Western-Food-34 | Custom 80-class
-# Text AI:       OpenRouter API
-# =============================================================================
-
-from flask import Flask, render_template, request, jsonify
-from transformers import pipeline, AutoImageProcessor, AutoModelForImageClassification
-from PIL import Image
-import torch
-import functools
-import os
-import re
-import requests
-import json
-from werkzeug.utils import secure_filename
-
-app = Flask(__name__)
-app.config["UPLOAD_FOLDER"] = "static/uploads"
-app.config["MAX_CONTENT_LENGTH"] = 16 * 1024 * 1024
-app.config["ALLOWED_EXTENSIONS"] = {'png', 'jpg', 'jpeg', 'webp'}
-os.makedirs(app.config["UPLOAD_FOLDER"], exist_ok=True)
-
-# ============================================================
-# 🔑  OPENROUTER CONFIG
-# ============================================================
-OPENROUTER_API_KEY = "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"
-OPENROUTER_URL     = "https://openrouter.ai/api/v1/chat/completions"
-
-CANDIDATE_MODELS = [
-    "openai/gpt-4o-mini",
-    "mistralai/mistral-7b-instruct:free",
-    "google/gemma-2-9b-it:free",
-]
-
-# ================================
-# 🔹 UTILITIES
-# ================================
-def allowed_file(filename):
-    return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config["ALLOWED_EXTENSIONS"]
-
-def calculate_bmi(height, weight):
-    h = height / 100
-    return round(weight / (h ** 2), 1)
-
-def get_bmi_category(bmi):
-    if bmi < 18.5:   return "Underweight"
-    elif bmi < 25.0: return "Normal weight"
-    elif bmi < 30.0: return "Overweight"
-    else:            return "Obese"
-
-def call_openrouter(prompt, max_tokens=1000):
-    headers = {
-        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
-        "Content-Type":  "application/json",
-        "HTTP-Referer":  "https://nutrivision.ai",
-        "X-Title":       "NutriVision",
-    }
-    for model in CANDIDATE_MODELS:
-        print(f"   🔷 Trying model: {model}")
-        try:
-            payload = {
-                "model":       model,
-                "messages":    [{"role": "user", "content": prompt}],
-                "max_tokens":  max_tokens,
-                "temperature": 0.4,
-            }
-            resp = requests.post(OPENROUTER_URL, headers=headers, json=payload, timeout=45)
-            print(f"      HTTP {resp.status_code}")
-            if resp.status_code != 200:
-                print(f"      ❌ Error: {resp.text[:300]}")
-                continue
-            content = resp.json().get("choices", [{}])[0].get("message", {}).get("content", "").strip()
-            if not content:
-                print(f"      ❌ Empty content from {model}")
-                continue
-            print(f"      ✅ Got {len(content)} chars from {model}")
-            return content, model
-        except requests.exceptions.Timeout:
-            print(f"      ❌ Timeout on {model}")
-        except Exception as e:
-            print(f"      ❌ Exception on {model}: {e}")
-    print("   ❌ All models failed")
-    return None, None
-
-# ================================
-# 🔹 MODEL 1: nateraw/food
-# ================================
-@functools.lru_cache(maxsize=1)
-def load_food101_classifier():
-    print("🔄 [Model 1] Loading nateraw/food …")
-    return pipeline("image-classification", model="nateraw/food",
-                    device=0 if torch.cuda.is_available() else -1)
-
-# ================================
-# 🔹 MODEL 2: Indian-Western-Food-34
-# ================================
-@functools.lru_cache(maxsize=1)
-def load_indian_western_classifier():
-    print("🔄 [Model 2] Loading prithivMLmods/Indian-Western-Food-34 …")
-    return pipeline("image-classification",
-                    model="prithivMLmods/Indian-Western-Food-34",
-                    device=0 if torch.cuda.is_available() else -1)
-
-# ================================
-# 🔹 MODEL 3: Custom Fine-Tuned
-# ================================
-@functools.lru_cache(maxsize=1)
-def load_custom_model():
-    MODEL_PATH = "C:/Users/nages/Documents/nutrivision-ai/final_model"
-    print("🔄 [Model 3] Loading custom fine-tuned model …")
-    try:
-        proc = AutoImageProcessor.from_pretrained(MODEL_PATH)
-        mdl  = AutoModelForImageClassification.from_pretrained(
-            MODEL_PATH,
-            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
-        )
-        mdl.eval()
-        if torch.cuda.is_available():
-            mdl = mdl.cuda()
-        print("✅ [Model 3] Loaded!")
-        return proc, mdl
-    except Exception as e:
-        print(f"⚠️  [Model 3] Failed: {e}")
-        return None, None
-
-# ================================
-# 🔹 3-MODEL ENSEMBLE
-# ================================
-def detect_food(image_path):
-    image      = Image.open(image_path).convert('RGB')
-    candidates = []
-
-    try:
-        preds = load_food101_classifier()(image, top_k=3)
-        b = preds[0]
-        candidates.append({"food": b['label'].replace('_',' ').title(),
-                            "confidence": b['score'], "source": "Food-101"})
-        print(f"  ▸ Model 1  {b['label']}  {b['score']*100:.1f}%")
-    except Exception as e:
-        print(f"  ▸ Model 1 error: {e}")
-
-    try:
-        preds = load_indian_western_classifier()(image, top_k=3)
-        b = preds[0]
-        candidates.append({"food": b['label'].replace('_',' ').title(),
-                            "confidence": b['score'], "source": "Indian-Western-34"})
-        print(f"  ▸ Model 2  {b['label']}  {b['score']*100:.1f}%")
-    except Exception as e:
-        print(f"  ▸ Model 2 error: {e}")
-
-    try:
-        proc, mdl = load_custom_model()
-        if proc and mdl:
-            inputs = proc(images=image, return_tensors="pt")
-            if torch.cuda.is_available():
-                inputs = {k: v.cuda() for k, v in inputs.items()}
-            with torch.no_grad():
-                logits = mdl(**inputs).logits
-            pid  = logits.argmax(-1).item()
-            conf = torch.softmax(logits, dim=-1)[0][pid].item()
-            lbl  = mdl.config.id2label[pid]
-            candidates.append({"food": lbl.replace('_',' ').title(),
-                                "confidence": conf, "source": "Custom-80"})
-            print(f"  ▸ Model 3  {lbl}  {conf*100:.1f}%")
-    except Exception as e:
-        print(f"  ▸ Model 3 error: {e}")
-
-    if not candidates:
-        return "Unknown Food", 0.0, "No model available"
-
-    winner = max(candidates, key=lambda x: x["confidence"])
-    print(f"✅ Winner → {winner['food']}  {winner['confidence']*100:.1f}%  [{winner['source']}]")
-    return winner["food"], winner["confidence"], winner["source"]
-
-# ================================
-# 🔹 LLM: FULL NUTRITION REPORT
-# ================================
-def generate_full_report(food_name, age, gender, height, weight,
-                          bmi, bmi_category, condition, diet_pref):
-    cond_str = condition if condition and condition.lower() != "none" else "None"
-    print(f"\n🔶 generate_full_report() → food={food_name}, condition={cond_str}, bmi={bmi_category}")
-
-    prompt = f"""You are a certified nutritionist AI. Return ONLY a raw JSON object — no markdown, no code fences, no explanation, no extra text whatsoever. Start your response with {{ and end with }}.
-
-You are analyzing: {food_name}
-
-User details:
-- Age: {age}, Gender: {gender}
-- Height: {height}cm, Weight: {weight}kg
-- BMI: {bmi} which is {bmi_category}
-- Diet: {diet_pref}
-- Health condition: {cond_str}
-
-Fill this JSON with REAL, SPECIFIC data for {food_name}. Every field must be specific to {food_name} — never give generic values.
-
-{{
-  "nutrition": {{
-    "serving_size": "<typical serving size of {food_name}>",
-    "calories": "<real calories of {food_name} per serving>",
-    "protein": "<real protein of {food_name}>",
-    "carbohydrates": "<real carbs of {food_name}>",
-    "fat": "<real fat of {food_name}>",
-    "fiber": "<real fiber of {food_name}>",
-    "sugar": "<real sugar of {food_name}>",
-    "sodium": "<real sodium of {food_name}>"
-  }},
-  "health_benefits": [
-    "<benefit 1 specific to {food_name}>",
-    "<benefit 2 specific to {food_name}>",
-    "<benefit 3 specific to {food_name}>"
-  ],
-  "portion_advice": "<how much {food_name} should a {age}-year-old {gender} with {bmi_category} BMI and {cond_str} eat>",
-  "health_context": "<specific explanation of how {food_name} affects {cond_str} — mention key nutrients and why they matter for {cond_str}>",
-  "alternatives": [
-    {{"name": "<healthier alternative to {food_name}>", "reason": "<why better for {cond_str} and {bmi_category}>"}},
-    {{"name": "<healthier alternative to {food_name}>", "reason": "<why better for {cond_str} and {bmi_category}>"}},
-    {{"name": "<healthier alternative to {food_name}>", "reason": "<why better for {cond_str} and {bmi_category}>"}}
-  ]
-}}"""
-
-    raw, model_used = call_openrouter(prompt, max_tokens=1000)
-    if not raw:
-        print("⚠️  All LLM calls failed → using fallback")
-        return None
-
-    print(f"   Model used: {model_used}")
-    print(f"   Raw (first 400 chars): {raw[:400]}")
-
-    try:
-        clean = raw.strip()
-        clean = re.sub(r"^```[a-zA-Z]*\n?", "", clean)
-        clean = re.sub(r"\n?```$", "", clean.strip())
-        m = re.search(r'\{.*\}', clean, re.DOTALL)
-        if m:
-            clean = m.group(0)
-        parsed = json.loads(clean)
-        print(f"✅ JSON parsed OK — calories={parsed.get('nutrition',{}).get('calories','?')}")
-        return parsed
-    except Exception as e:
-        print(f"⚠️  JSON parse error: {e}")
-        print(f"   Raw response: {raw[:600]}")
-        return None
-
-# ================================
-# 🔹 SHOPPING + DELIVERY URLS
-# ================================
-def get_shopping_urls(food_item):
-    """
-    Returns search links for grocery delivery + food delivery platforms.
-    Uses each platform's native search URL format.
-    """
-    raw       = food_item.strip()
-    q_pct     = raw.lower().replace(' ', '%20')   # URL percent-encoded
-    q_plus    = raw.lower().replace(' ', '+')      # + encoded (Google style)
-    q_dash    = raw.lower().replace(' ', '-')      # dash-separated (Swiggy)
-    q_zomato  = raw.lower().replace(' ', '%20')    # Zomato uses %20
-
-    return [
-        # ── Grocery / delivery platforms ──────────────────────────────
-        {
-            "platform": "BigBasket",
-            "url":      f"https://www.bigbasket.com/ps/?q={q_pct}",
-            "emoji":    "🛒",
-            "category": "grocery"
-        },
-        {
-            "platform": "Blinkit",
-            "url":      f"https://blinkit.com/s/?q={q_pct}",
-            "emoji":    "⚡",
-            "category": "grocery"
-        },
-        {
-            "platform": "Amazon",
-            "url":      f"https://www.amazon.in/s?k={q_plus}+food",
-            "emoji":    "📦",
-            "category": "grocery"
-        },
-        {
-            "platform": "Flipkart",
-            "url":      f"https://www.flipkart.com/search?q={q_pct}",
-            "emoji":    "🛍️",
-            "category": "grocery"
-        },
-        # ── Food delivery platforms ────────────────────────────────────
-        {
-            "platform": "Swiggy",
-            "url":      f"https://www.swiggy.com/search?query={q_pct}",
-            "emoji":    "🍊",
-            "category": "delivery"
-        },
-        {
-            "platform": "Zomato",
-            "url":      f"https://www.zomato.com/search?q={q_zomato}",
-            "emoji":    "🔴",
-            "category": "delivery"
-        },
-    ]
-
-# ================================
-# 🔹 FALLBACK REPORT
-# ================================
-def fallback_report(food_name="this food"):
-    return {
-        "nutrition": {
-            "serving_size": "1 standard serving (~150g)",
-            "calories": "~250 kcal", "protein": "~8g",
-            "carbohydrates": "~35g", "fat": "~10g",
-            "fiber": "~3g", "sugar": "~5g", "sodium": "~200mg"
-        },
-        "health_benefits": [
-            f"{food_name} provides essential macronutrients for daily energy.",
-            "Contains dietary fiber supporting digestive health.",
-            "Source of micronutrients important for body functions."
-        ],
-        "portion_advice": f"Consume 1 standard serving of {food_name} as part of a balanced diet.",
-        "health_context": f"Consult a nutritionist for personalised advice about {food_name} and your health goals.",
-        "alternatives": [
-            {"name": "Steamed Vegetables", "reason": "Low calories, high fiber and nutrients"},
-            {"name": "Grilled Chicken",    "reason": "Lean protein, low in saturated fat"},
-            {"name": "Fresh Fruit Bowl",   "reason": "Natural sugars with vitamins and antioxidants"}
-        ]
-    }
-
-# ================================
-# 🔹 ROUTES
-# ================================
-@app.route("/")
-def home():
-    return render_template("home.html")
-
-@app.route("/analyzer")
-def analyzer():
-    return render_template("index.html")
-
-@app.route("/about")
-def about():
-    return render_template("about.html")
-
-@app.route("/analyze", methods=["POST"])
-def analyze():
-    try:
-        if 'image' not in request.files:
-            return jsonify({"error": "No image uploaded"}), 400
-        image_file = request.files['image']
-        if not image_file.filename or not allowed_file(image_file.filename):
-            return jsonify({"error": "Invalid file type. Use PNG, JPG, JPEG or WebP."}), 400
-
-        age       = request.form.get("age", "25")
-        gender    = request.form.get("gender", "Male")
-        height    = float(request.form.get("height", "170"))
-        weight    = float(request.form.get("weight", "70"))
-        diet_pref = request.form.get("preference", "Vegetarian")
-        condition = request.form.get("condition", "None")
-
-        bmi          = calculate_bmi(height, weight)
-        bmi_category = get_bmi_category(bmi)
-
-        filename = secure_filename(image_file.filename)
-        img_path = os.path.join(app.config["UPLOAD_FOLDER"], filename)
-        image_file.save(img_path)
-
-        print("\n" + "="*55)
-        print(f"📥 REQUEST: {age}y {gender}, h={height} w={weight}, BMI={bmi} ({bmi_category})")
-        print(f"   condition={condition}, diet={diet_pref}")
-
-        print("\n━━━ 3-MODEL ENSEMBLE ━━━")
-        food_name, confidence, detection_source = detect_food(img_path)
-
-        print("\n━━━ LLM NUTRITION REPORT ━━━")
-        report = generate_full_report(
-            food_name, age, gender, height, weight,
-            bmi, bmi_category, condition, diet_pref
-        )
-
-        if report is None:
-            print("⚠️  Using FALLBACK")
-            report = fallback_report(food_name)
-
-        alternatives = [
-            {"name": a["name"], "reason": a["reason"],
-             "urls": get_shopping_urls(a["name"])}
-            for a in report.get("alternatives", [])
-        ]
-
-        return jsonify({
-            "food":             food_name,
-            "confidence":       f"{confidence * 100:.1f}%",
-            "detection_source": detection_source,
-            "bmi":              bmi,
-            "bmi_category":     bmi_category,
-            "nutrition":        report.get("nutrition", {}),
-            "health_benefits":  report.get("health_benefits", []),
-            "portion_advice":   report.get("portion_advice", "1 standard serving"),
-            "health_context":   report.get("health_context", ""),
-            "alternatives":     alternatives,
-        })
-
-    except Exception as e:
-        import traceback; traceback.print_exc()
-        return jsonify({"error": f"Analysis failed: {str(e)}"}), 500
-
-if __name__ == "__main__":
-    print("🚀 NutriVision starting…")
-    print(f"🎮 GPU: {torch.cuda.is_available()}")
-    print(f"🔑 OpenRouter key: {OPENROUTER_API_KEY[:18]}...")
-    print(f"🤖 Model priority: {CANDIDATE_MODELS}")
+# =============================================================================
+# 🥗 NutriVision - app.py
+# Vision Models: nateraw/food | prithivMLmods/Indian-Western-Food-34 | Custom 80-class
+# Text AI:       OpenRouter API
+# =============================================================================
+
+from flask import Flask, render_template, request, jsonify
+from transformers import pipeline, AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+import functools
+import os
+import re
+import requests
+import json
+from werkzeug.utils import secure_filename
+
+app = Flask(__name__)
+app.config["UPLOAD_FOLDER"] = "static/uploads"
+app.config["MAX_CONTENT_LENGTH"] = 16 * 1024 * 1024
+app.config["ALLOWED_EXTENSIONS"] = {'png', 'jpg', 'jpeg', 'webp'}
+os.makedirs(app.config["UPLOAD_FOLDER"], exist_ok=True)
+
+# ============================================================
+# 🔑  OPENROUTER CONFIG
+# ============================================================
+OPENROUTER_API_KEY = "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"
+OPENROUTER_URL     = "https://openrouter.ai/api/v1/chat/completions"
+
+CANDIDATE_MODELS = [
+    "openai/gpt-4o-mini",
+    "mistralai/mistral-7b-instruct:free",
+    "google/gemma-2-9b-it:free",
+]
+
+# ================================
+# 🔹 UTILITIES
+# ================================
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config["ALLOWED_EXTENSIONS"]
+
+def calculate_bmi(height, weight):
+    h = height / 100
+    return round(weight / (h ** 2), 1)
+
+def get_bmi_category(bmi):
+    if bmi < 18.5:   return "Underweight"
+    elif bmi < 25.0: return "Normal weight"
+    elif bmi < 30.0: return "Overweight"
+    else:            return "Obese"
+
+def call_openrouter(prompt, max_tokens=1000):
+    headers = {
+        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
+        "Content-Type":  "application/json",
+        "HTTP-Referer":  "https://nutrivision.ai",
+        "X-Title":       "NutriVision",
+    }
+    for model in CANDIDATE_MODELS:
+        print(f"   🔷 Trying model: {model}")
+        try:
+            payload = {
+                "model":       model,
+                "messages":    [{"role": "user", "content": prompt}],
+                "max_tokens":  max_tokens,
+                "temperature": 0.4,
+            }
+            resp = requests.post(OPENROUTER_URL, headers=headers, json=payload, timeout=45)
+            print(f"      HTTP {resp.status_code}")
+            if resp.status_code != 200:
+                print(f"      ❌ Error: {resp.text[:300]}")
+                continue
+            content = resp.json().get("choices", [{}])[0].get("message", {}).get("content", "").strip()
+            if not content:
+                print(f"      ❌ Empty content from {model}")
+                continue
+            print(f"      ✅ Got {len(content)} chars from {model}")
+            return content, model
+        except requests.exceptions.Timeout:
+            print(f"      ❌ Timeout on {model}")
+        except Exception as e:
+            print(f"      ❌ Exception on {model}: {e}")
+    print("   ❌ All models failed")
+    return None, None
+
+# ================================
+# 🔹 MODEL 1: nateraw/food
+# ================================
+@functools.lru_cache(maxsize=1)
+def load_food101_classifier():
+    print("🔄 [Model 1] Loading nateraw/food …")
+    return pipeline("image-classification", model="nateraw/food",
+                    device=0 if torch.cuda.is_available() else -1)
+
+# ================================
+# 🔹 MODEL 2: Indian-Western-Food-34
+# ================================
+@functools.lru_cache(maxsize=1)
+def load_indian_western_classifier():
+    print("🔄 [Model 2] Loading prithivMLmods/Indian-Western-Food-34 …")
+    return pipeline("image-classification",
+                    model="prithivMLmods/Indian-Western-Food-34",
+                    device=0 if torch.cuda.is_available() else -1)
+
+# ================================
+# 🔹 MODEL 3: Custom Fine-Tuned
+# ================================
+@functools.lru_cache(maxsize=1)
+def load_custom_model():
+    MODEL_PATH = "final_model"
+    print("🔄 [Model 3] Loading custom fine-tuned model …")
+    try:
+        proc = AutoImageProcessor.from_pretrained(MODEL_PATH)
+        mdl  = AutoModelForImageClassification.from_pretrained(
+            MODEL_PATH,
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+        )
+        mdl.eval()
+        if torch.cuda.is_available():
+            mdl = mdl.cuda()
+        print("✅ [Model 3] Loaded!")
+        return proc, mdl
+    except Exception as e:
+        print(f"⚠️  [Model 3] Failed: {e}")
+        return None, None
+
+# ================================
+# 🔹 3-MODEL ENSEMBLE
+# ================================
+def detect_food(image_path):
+    image      = Image.open(image_path).convert('RGB')
+    candidates = []
+
+    try:
+        preds = load_food101_classifier()(image, top_k=3)
+        b = preds[0]
+        candidates.append({"food": b['label'].replace('_',' ').title(),
+                            "confidence": b['score'], "source": "Food-101"})
+        print(f"  ▸ Model 1  {b['label']}  {b['score']*100:.1f}%")
+    except Exception as e:
+        print(f"  ▸ Model 1 error: {e}")
+
+    try:
+        preds = load_indian_western_classifier()(image, top_k=3)
+        b = preds[0]
+        candidates.append({"food": b['label'].replace('_',' ').title(),
+                            "confidence": b['score'], "source": "Indian-Western-34"})
+        print(f"  ▸ Model 2  {b['label']}  {b['score']*100:.1f}%")
+    except Exception as e:
+        print(f"  ▸ Model 2 error: {e}")
+
+    try:
+        proc, mdl = load_custom_model()
+        if proc and mdl:
+            inputs = proc(images=image, return_tensors="pt")
+            if torch.cuda.is_available():
+                inputs = {k: v.cuda() for k, v in inputs.items()}
+            with torch.no_grad():
+                logits = mdl(**inputs).logits
+            pid  = logits.argmax(-1).item()
+            conf = torch.softmax(logits, dim=-1)[0][pid].item()
+            lbl  = mdl.config.id2label[pid]
+            candidates.append({"food": lbl.replace('_',' ').title(),
+                                "confidence": conf, "source": "Custom-80"})
+            print(f"  ▸ Model 3  {lbl}  {conf*100:.1f}%")
+    except Exception as e:
+        print(f"  ▸ Model 3 error: {e}")
+
+    if not candidates:
+        return "Unknown Food", 0.0, "No model available"
+
+    winner = max(candidates, key=lambda x: x["confidence"])
+    print(f"✅ Winner → {winner['food']}  {winner['confidence']*100:.1f}%  [{winner['source']}]")
+    return winner["food"], winner["confidence"], winner["source"]
+
+# ================================
+# 🔹 LLM: FULL NUTRITION REPORT
+# ================================
+def generate_full_report(food_name, age, gender, height, weight,
+                          bmi, bmi_category, condition, diet_pref):
+    cond_str = condition if condition and condition.lower() != "none" else "None"
+    print(f"\n🔶 generate_full_report() → food={food_name}, condition={cond_str}, bmi={bmi_category}")
+
+    prompt = f"""You are a certified nutritionist AI. Return ONLY a raw JSON object — no markdown, no code fences, no explanation, no extra text whatsoever. Start your response with {{ and end with }}.
+
+You are analyzing: {food_name}
+
+User details:
+- Age: {age}, Gender: {gender}
+- Height: {height}cm, Weight: {weight}kg
+- BMI: {bmi} which is {bmi_category}
+- Diet: {diet_pref}
+- Health condition: {cond_str}
+
+Fill this JSON with REAL, SPECIFIC data for {food_name}. Every field must be specific to {food_name} — never give generic values.
+
+{{
+  "nutrition": {{
+    "serving_size": "<typical serving size of {food_name}>",
+    "calories": "<real calories of {food_name} per serving>",
+    "protein": "<real protein of {food_name}>",
+    "carbohydrates": "<real carbs of {food_name}>",
+    "fat": "<real fat of {food_name}>",
+    "fiber": "<real fiber of {food_name}>",
+    "sugar": "<real sugar of {food_name}>",
+    "sodium": "<real sodium of {food_name}>"
+  }},
+  "health_benefits": [
+    "<benefit 1 specific to {food_name}>",
+    "<benefit 2 specific to {food_name}>",
+    "<benefit 3 specific to {food_name}>"
+  ],
+  "portion_advice": "<how much {food_name} should a {age}-year-old {gender} with {bmi_category} BMI and {cond_str} eat>",
+  "health_context": "<specific explanation of how {food_name} affects {cond_str} — mention key nutrients and why they matter for {cond_str}>",
+  "alternatives": [
+    {{"name": "<healthier alternative to {food_name}>", "reason": "<why better for {cond_str} and {bmi_category}>"}},
+    {{"name": "<healthier alternative to {food_name}>", "reason": "<why better for {cond_str} and {bmi_category}>"}},
+    {{"name": "<healthier alternative to {food_name}>", "reason": "<why better for {cond_str} and {bmi_category}>"}}
+  ]
+}}"""
+
+    raw, model_used = call_openrouter(prompt, max_tokens=1000)
+    if not raw:
+        print("⚠️  All LLM calls failed → using fallback")
+        return None
+
+    print(f"   Model used: {model_used}")
+    print(f"   Raw (first 400 chars): {raw[:400]}")
+
+    try:
+        clean = raw.strip()
+        clean = re.sub(r"^```[a-zA-Z]*\n?", "", clean)
+        clean = re.sub(r"\n?```$", "", clean.strip())
+        m = re.search(r'\{.*\}', clean, re.DOTALL)
+        if m:
+            clean = m.group(0)
+        parsed = json.loads(clean)
+        print(f"✅ JSON parsed OK — calories={parsed.get('nutrition',{}).get('calories','?')}")
+        return parsed
+    except Exception as e:
+        print(f"⚠️  JSON parse error: {e}")
+        print(f"   Raw response: {raw[:600]}")
+        return None
+
+# ================================
+# 🔹 SHOPPING + DELIVERY URLS
+# ================================
+def get_shopping_urls(food_item):
+    """
+    Returns search links for grocery delivery + food delivery platforms.
+    Uses each platform's native search URL format.
+    """
+    raw       = food_item.strip()
+    q_pct     = raw.lower().replace(' ', '%20')   # URL percent-encoded
+    q_plus    = raw.lower().replace(' ', '+')      # + encoded (Google style)
+    q_dash    = raw.lower().replace(' ', '-')      # dash-separated (Swiggy)
+    q_zomato  = raw.lower().replace(' ', '%20')    # Zomato uses %20
+
+    return [
+        # ── Grocery / delivery platforms ──────────────────────────────
+        {
+            "platform": "BigBasket",
+            "url":      f"https://www.bigbasket.com/ps/?q={q_pct}",
+            "emoji":    "🛒",
+            "category": "grocery"
+        },
+        {
+            "platform": "Blinkit",
+            "url":      f"https://blinkit.com/s/?q={q_pct}",
+            "emoji":    "⚡",
+            "category": "grocery"
+        },
+        {
+            "platform": "Amazon",
+            "url":      f"https://www.amazon.in/s?k={q_plus}+food",
+            "emoji":    "📦",
+            "category": "grocery"
+        },
+        {
+            "platform": "Flipkart",
+            "url":      f"https://www.flipkart.com/search?q={q_pct}",
+            "emoji":    "🛍️",
+            "category": "grocery"
+        },
+        # ── Food delivery platforms ────────────────────────────────────
+        {
+            "platform": "Swiggy",
+            "url":      f"https://www.swiggy.com/search?query={q_pct}",
+            "emoji":    "🍊",
+            "category": "delivery"
+        },
+        {
+            "platform": "Zomato",
+            "url":      f"https://www.zomato.com/search?q={q_zomato}",
+            "emoji":    "🔴",
+            "category": "delivery"
+        },
+    ]
+
+# ================================
+# 🔹 FALLBACK REPORT
+# ================================
+def fallback_report(food_name="this food"):
+    return {
+        "nutrition": {
+            "serving_size": "1 standard serving (~150g)",
+            "calories": "~250 kcal", "protein": "~8g",
+            "carbohydrates": "~35g", "fat": "~10g",
+            "fiber": "~3g", "sugar": "~5g", "sodium": "~200mg"
+        },
+        "health_benefits": [
+            f"{food_name} provides essential macronutrients for daily energy.",
+            "Contains dietary fiber supporting digestive health.",
+            "Source of micronutrients important for body functions."
+        ],
+        "portion_advice": f"Consume 1 standard serving of {food_name} as part of a balanced diet.",
+        "health_context": f"Consult a nutritionist for personalised advice about {food_name} and your health goals.",
+        "alternatives": [
+            {"name": "Steamed Vegetables", "reason": "Low calories, high fiber and nutrients"},
+            {"name": "Grilled Chicken",    "reason": "Lean protein, low in saturated fat"},
+            {"name": "Fresh Fruit Bowl",   "reason": "Natural sugars with vitamins and antioxidants"}
+        ]
+    }
+
+# ================================
+# 🔹 ROUTES
+# ================================
+@app.route("/")
+def home():
+    return render_template("home.html")
+
+@app.route("/analyzer")
+def analyzer():
+    return render_template("index.html")
+
+@app.route("/about")
+def about():
+    return render_template("about.html")
+
+@app.route("/analyze", methods=["POST"])
+def analyze():
+    try:
+        if 'image' not in request.files:
+            return jsonify({"error": "No image uploaded"}), 400
+        image_file = request.files['image']
+        if not image_file.filename or not allowed_file(image_file.filename):
+            return jsonify({"error": "Invalid file type. Use PNG, JPG, JPEG or WebP."}), 400
+
+        age       = request.form.get("age", "25")
+        gender    = request.form.get("gender", "Male")
+        height    = float(request.form.get("height", "170"))
+        weight    = float(request.form.get("weight", "70"))
+        diet_pref = request.form.get("preference", "Vegetarian")
+        condition = request.form.get("condition", "None")
+
+        bmi          = calculate_bmi(height, weight)
+        bmi_category = get_bmi_category(bmi)
+
+        filename = secure_filename(image_file.filename)
+        img_path = os.path.join(app.config["UPLOAD_FOLDER"], filename)
+        image_file.save(img_path)
+
+        print("\n" + "="*55)
+        print(f"📥 REQUEST: {age}y {gender}, h={height} w={weight}, BMI={bmi} ({bmi_category})")
+        print(f"   condition={condition}, diet={diet_pref}")
+
+        print("\n━━━ 3-MODEL ENSEMBLE ━━━")
+        food_name, confidence, detection_source = detect_food(img_path)
+
+        print("\n━━━ LLM NUTRITION REPORT ━━━")
+        report = generate_full_report(
+            food_name, age, gender, height, weight,
+            bmi, bmi_category, condition, diet_pref
+        )
+
+        if report is None:
+            print("⚠️  Using FALLBACK")
+            report = fallback_report(food_name)
+
+        alternatives = [
+            {"name": a["name"], "reason": a["reason"],
+             "urls": get_shopping_urls(a["name"])}
+            for a in report.get("alternatives", [])
+        ]
+
+        return jsonify({
+            "food":             food_name,
+            "confidence":       f"{confidence * 100:.1f}%",
+            "detection_source": detection_source,
+            "bmi":              bmi,
+            "bmi_category":     bmi_category,
+            "nutrition":        report.get("nutrition", {}),
+            "health_benefits":  report.get("health_benefits", []),
+            "portion_advice":   report.get("portion_advice", "1 standard serving"),
+            "health_context":   report.get("health_context", ""),
+            "alternatives":     alternatives,
+        })
+
+    except Exception as e:
+        import traceback; traceback.print_exc()
+        return jsonify({"error": f"Analysis failed: {str(e)}"}), 500
+
+if __name__ == "__main__":
+    print("🚀 NutriVision starting…")
+    print(f"🎮 GPU: {torch.cuda.is_available()}")
+    print(f"🔑 OpenRouter key: {OPENROUTER_API_KEY[:18]}...")
+    print(f"🤖 Model priority: {CANDIDATE_MODELS}")
     app.run(host="0.0.0.0", port=7860)