Update app.py

app.py

@@ -1,176 +1,198 @@
-# app.py
 import gradio as gr
 import easyocr
-import trimesh
 import numpy as np
 from PIL import Image
 import os
-import random
-import time
+import folium
+from huggingface_hub import InferenceClient
 
-# --- SETUP ---
-# Create folders to store generated 3D models
-os.makedirs("models", exist_ok=True)
+# --- CONFIGURATION ---
+# Replace with your actual token
+HF_TOKEN = "hf_..." 
 
-# Initialize OCR Reader (English)
+# Initialize Tools
 reader = easyocr.Reader(['en'])
 
-# --- LOGIC 1: 3D GENERATOR (2D to 3D Card) ---
-def create_3d_card(image, food_name):
+# --- LOGIC 1: AI HEALTH ANALYZER & IMAGE GENERATOR ---
+def analyze_food(food_name, language, api_key):
     """
-    Takes an image (numpy array or path) and converts it to a standing 3D GLB file.
+    Generates a real-world image AND detailed health analysis in the selected language.
     """
+    if not food_name:
+        return None, "Please select a food item.", ""
+
+    token = api_key if api_key else HF_TOKEN
+    client = InferenceClient(token=token)
+    
+    # 1. Generate Image (Visual)
+    img_prompt = (
+        f"Professional food photography of {food_name}, "
+        "8k resolution, hyperrealistic, cinematic lighting, "
+        "macro details, steam rising, delicious, gourmet plating, "
+        "unreal engine 5 render style, depth of field."
+    )
+    
     try:
-        # Convert numpy array to Image if needed
-        if isinstance(image, np.ndarray):
-            img = Image.fromarray(image).convert('RGB')
-        else:
-            img = Image.open(image).convert('RGB')
-
-        # 1. Create 3D Board
-        width, height = img.size
-        aspect = width / height
-        # Create a thin box (The "Card")
-        mesh = trimesh.creation.box(extents=[aspect, 1.0, 0.05])
-
-        # 2. Apply Texture
-        material = trimesh.visual.texture.SimpleMaterial(image=img)
-        uv = np.zeros((24, 2))
-        # Map front face to image
-        uv[0:4] = [[0, 0], [1, 0], [1, 1], [0, 1]] 
-        mesh.visual = trimesh.visual.TextureVisuals(uv=uv, image=img, material=material)
-
-        # 3. Rotate to stand up (90 deg on X)
-        rot = trimesh.transformations.rotation_matrix(np.radians(90), [1, 0, 0])
-        mesh.apply_transform(rot)
-        
-        # 4. Save
-        filename = f"models/{food_name.replace(' ', '_')}_{int(time.time())}.glb"
-        mesh.export(filename)
-        return filename
+        print(f"Generating image for {food_name}...")
+        generated_image = client.text_to_image(
+            prompt=img_prompt,
+            model="stabilityai/stable-diffusion-xl-base-1.0",
+            negative_prompt="cartoon, drawing, anime, text, blurry, low quality",
+            width=1024, height=1024
+        )
+    except Exception as e:
+        print(f"Image Error: {e}")
+        generated_image = None
+
+    # 2. Generate Health Info (Text) in Selected Language
+    text_prompt = (
+        f"Act as a nutritionist. Analyze the food item '{food_name}'. "
+        f"Provide the response in {language} language. "
+        "Format the response strictly with two sections:\n"
+        "1. Health Benefits\n"
+        "2. Potential Consequences or Cons (e.g., high calories, allergies).\n"
+        "Keep it concise and bulleted."
+    )
+    
+    try:
+        response = client.text_generation(
+            prompt=text_prompt,
+            model="tiiuae/falcon-7b-instruct", # Using a fast text model
+            max_new_tokens=400,
+            temperature=0.7
+        )
+        health_info = response
     except Exception as e:
-        print(f"Error: {e}")
-        return None
+        health_info = f"Could not retrieve health data: {e}"
 
-# --- LOGIC 2: MENU SCANNING (OCR) ---
+    return generated_image, health_info
+
+# --- LOGIC 2: INTERACTIVE MAP (SCROLLABLE) ---
+def get_map_html(location_name="Bahawalpur"):
+    """
+    Creates an interactive HTML map centered on a location.
+    """
+    # Default coordinates (Bahawalpur)
+    start_coords = [29.3544, 71.6911]
+    
+    # If user searches specific place, we could geocode it (Mocking change for demo)
+    if location_name.lower() == "islamabad":
+        start_coords = [33.6844, 73.0479]
+    elif location_name.lower() == "lahore":
+        start_coords = [31.5497, 74.3436]
+
+    # Create Map
+    m = folium.Map(location=start_coords, zoom_start=13)
+    
+    # Add Marker
+    folium.Marker(
+        start_coords, 
+        popup=f"<i>{location_name}</i>", 
+        tooltip="Click me!"
+    ).add_to(m)
+
+    return m._repr_html_()
+
+# --- LOGIC 3: MENU SCANNING ---
 def scan_menu(image):
     if image is None:
         return "Please upload an image.", []
     
-    # 1. Read text
     results = reader.readtext(image)
-    text_list = [res[1] for res in results]
+    # Filter for food-like text (longer than 3 chars, not numbers)
+    detected_items = [res[1] for res in results if len(res[1]) > 3 and not res[1].isdigit()]
     
-    # 2. Filter for things that look like food (Simple logic for demo)
-    # In a real app, you'd verify against a food database.
-    detected_items = [text for text in text_list if len(text) > 3 and not text.isdigit()]
-    
-    if not detected_items:
-        return "No readable text found.", []
-        
     status = f"✅ Found {len(detected_items)} items!"
-    # Return status and update the Dropdown choices
     return status, gr.update(choices=detected_items, value=detected_items[0] if detected_items else None)
 
-# --- LOGIC 3: GENERATE AR VIEW ---
-def generate_ar(selected_item, menu_image):
-    # For this demo, we use the WHOLE menu image as the texture for the 3D card.
-    # In a pro version, we would crop the specific part of the image.
-    if menu_image is None:
-        return None, "Please upload a menu first."
-        
-    glb_path = create_3d_card(menu_image, selected_item)
-    
-    return glb_path, f"✨ 3D Model for '{selected_item}' created! Download to view in AR."
-
-# --- LOGIC 4: GOOGLE MAPS SIMULATOR ---
-def search_maps(place_name):
-    # Simulating a Google Maps API call
-    time.sleep(1) # Fake loading
+# --- UI LAYOUT ---
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="gray")) as demo:
     
-    # Mock Menu Images (In real life, this would scrape the place's photos)
-    # We return a placeholder image for the demo
-    return [
-        ("https://cdn-icons-png.flaticon.com/512/1904/1904221.png", "Digital Menu Found")
-    ], f"📍 Found location: {place_name}. Menu retrieved."
-
-# --- LOGIC 5: AI CHAT ---
-def chat_response(message, history):
-    message = message.lower()
-    if "price" in message:
-        return "Based on the menu scan, prices usually range from $10-$20."
-    elif "recommend" in message:
-        return "The Burgers seem popular at this location based on reviews!"
-    elif "hello" in message:
-        return "Hello! Upload a menu or search a place, and I'll help you visualize the food."
-    else:
-        return "I can help you analyze the menu or generate 3D previews. Just ask!"
-
-# --- UI BUILDING (GRADIO) ---
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🥗 MenuVision AI: Health & visual Analyzer")
     
-    gr.Markdown(
-        """
-        # 🍔 MenuVision AI
-        ### AI-Powered Menu Scanner, Google Maps Finder & AR Visualizer
-        """
-    )
+    with gr.Row():
+        api_input = gr.Textbox(label="Hugging Face Token (Optional)", type="password", placeholder="Paste token here if not hardcoded")
+        language_drop = gr.Dropdown(label="🌐 Select Language", choices=["English", "Urdu", "French", "Spanish", "Arabic"], value="English")
 
     with gr.Tabs():
         
-        # TAB 1: SCAN MENU
-        with gr.TabItem("📸 Scan Menu"):
+        # --- TAB 1: SCAN & HEALTH ANALYSIS ---
+        with gr.TabItem("📸 Scan & Analyze"):
             with gr.Row():
-                with gr.Column():
-                    menu_input = gr.Image(type="numpy", label="Upload Menu Photo")
-                    scan_btn = gr.Button("🔍 Analyze Menu", variant="primary")
+                # LEFT COLUMN: INPUT
+                with gr.Column(scale=1):
+                    menu_input = gr.Image(type="numpy", label="1. Upload Menu Photo")
+                    scan_btn = gr.Button("🔍 Scan Text", variant="secondary")
+                    
+                    gr.Markdown("---")
+                    status_output = gr.Textbox(label="Status", interactive=False)
+                    food_dropdown = gr.Dropdown(label="2. Select Detected Food", choices=[])
+                    analyze_btn = gr.Button("✨ Analyze Health & Generate Image", variant="primary")
                 
-                with gr.Column():
-                    status_output = gr.Textbox(label="Status")
-                    food_dropdown = gr.Dropdown(label="Select Food Item", choices=[])
-                    generate_btn = gr.Button("✨ Create Real-World 3D", variant="stop")
-            
-            with gr.Row():
-                # The 3D Output
-                model_output = gr.Model3D(label="3D Preview (Rotate & Zoom)", clear_color=[1, 1, 1, 1])
-                download_msg = gr.Textbox(label="Instructions", value="Select an item and click Create. Then download the .glb file to view on your table.")
-
-        # TAB 2: GOOGLE MAPS
-        with gr.TabItem("🗺️ Google Maps Search"):
+                # RIGHT COLUMN: OUTPUT
+                with gr.Column(scale=2):
+                    # 1. Real World Image
+                    result_image = gr.Image(label="Real-World Representation", type="pil", height=400)
+                    
+                    # 2. Health Columns
+                    gr.Markdown("### 🩺 Nutritional Analysis")
+                    health_output = gr.Textbox(label="Benefits & Consequences", lines=10)
+
+        # --- TAB 2: INTERACTIVE MAPS ---
+        with gr.TabItem("🗺️ Interactive Map"):
             with gr.Row():
-                place_input = gr.Textbox(label="Search Restaurant/Cafe Name")
-                search_btn = gr.Button("Search Place")
+                place_search = gr.Textbox(label="Search Location (e.g., Islamabad)", placeholder="Type a city...")
+                map_btn = gr.Button("Update Map")
             
-            map_status = gr.Textbox(label="Result")
-            # We use a Gallery to mimic showing found menu photos
-            menu_gallery = gr.Gallery(label="Found Menu Images")
+            # This HTML component holds the interactive scrollable map
+            map_html = gr.HTML(value=get_map_html(), label="Scrollable Map")
 
-        # TAB 3: AI GUIDE
-        with gr.TabItem("💬 AI Assistant"):
-            chatbot = gr.ChatInterface(fn=chat_response, examples=["What is good here?", "Is it expensive?", "How do I use AR?"])
+        # --- TAB 3: ABOUT ME ---
+        with gr.TabItem("👨‍💻 About Developer"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    # Placeholder for your profile pic
+                    gr.Image("https://cdn-icons-png.flaticon.com/512/4140/4140048.png", width=200, show_label=False, show_download_button=False)
+                
+                with gr.Column(scale=3):
+                    gr.Markdown("""
+                    ### 👋 Hi, I'm Abdullah!
+                    
+                    **Computer Engineering Student | AI Enthusiast | Web Developer**
+                    
+                    I am currently an undergraduate student at **COMSATS University Islamabad**, specializing in Computer Engineering. 
+                    I have a passion for merging **Embedded Systems** with **Generative AI** to create real-world solutions.
+                    
+                    * **Role:** Intern Web Developer at MyK Global Forwarding.
+                    * **Focus:** TinyML, IoT, and GenAI Applications.
+                    * **Location:** Bahawalpur / Islamabad.
+                    
+                    **About MenuVision AI:**
+                    This project was designed to help people make better dietary choices by visualizing food from plain text menus and understanding the health implications immediately.
+                    """)
 
     # --- EVENT HANDLERS ---
     
-    # 1. Scan Menu -> Get Text -> Populate Dropdown
+    # 1. Scan Button
     scan_btn.click(
         fn=scan_menu,
         inputs=menu_input,
         outputs=[status_output, food_dropdown]
     )
 
-    # 2. Select Food -> Create 3D Model
-    generate_btn.click(
-        fn=generate_ar,
-        inputs=[food_dropdown, menu_input],
-        outputs=[model_output, download_msg]
+    # 2. Analyze Button (Image + Health Info)
+    analyze_btn.click(
+        fn=analyze_food,
+        inputs=[food_dropdown, language_drop, api_input],
+        outputs=[result_image, health_output]
     )
     
-    # 3. Maps Search -> Show Fake Menu
-    search_btn.click(
-        fn=search_maps,
-        inputs=place_input,
-        outputs=[menu_gallery, map_status]
+    # 3. Map Update
+    map_btn.click(
+        fn=get_map_html,
+        inputs=place_search,
+        outputs=map_html
     )
 
-# Launch
+# Launch App
 demo.launch()