Update app.py

app.py

@@ -1,150 +1,60 @@
+import os
 import gradio as gr
 import requests
-import os
-from fpdf import FPDF
-import uuid
-import re
-import matplotlib.pyplot as plt
-import tempfile
 
-# Load your Nebius API key and Folder ID from environment variables
-NEBIUS_API_KEY = os.getenv("NEBIUS_API_KEY") or "YOUR_NEBIUS_API_KEY"
-FOLDER_ID = os.getenv("FOLDER_ID") or "YOUR_FOLDER_ID"
+# Nebius environment variables from Hugging Face secrets
+FOLDER_ID = os.environ.get("FOLDER_ID")
+NEBIUS_API_KEY = os.environ.get("NEBIUS_API_KEY")
 
-def generate_meal(preferences, ingredients, time):
-    prompt = f"""
-You're a smart kitchen agent.
-Given:
-- Dietary preferences: {preferences}
-- Ingredients available: {ingredients}
-- Time available: {time} minutes
-Tasks:
-1. Suggest one meal idea
-2. Provide step-by-step recipe instructions
-3. List missing ingredients (Shopping List)
-4. Estimate nutrition (calories, protein, carbs, fat)
-Output in this format:
-Meal Name: ...
-Steps:
-1. ...
-2. ...
-Shopping List:
-- ...
-Nutrition:
-- Calories: ... kcal
-- Protein: ... g
-- Carbs: ... g
-- Fat: ... g
-"""
+# Function to call Nebius API
+def call_nebius(prompt):
+    url = "https://llm.api.cloud.yandex.net/foundationModels/v1/completion"
+    
     headers = {
-        "Authorization": f"Api-Key {NEBIUS_API_KEY}",
+        "Authorization": f"Bearer {NEBIUS_API_KEY}",
         "Content-Type": "application/json"
     }
+
     data = {
-        "modelUri": f"gpt://{FOLDER_ID}/yandexgpt-lite",
-        "completionOptions": {"stream": False, "temperature": 0.7, "maxTokens": 700},
-        "messages": [{"role": "user", "text": prompt.strip()}]
+        "modelUri": f"gpt://{FOLDER_ID}/yandexgpt/latest",
+        "completionOptions": {
+            "stream": False,
+            "temperature": 0.7,
+            "maxTokens": 500
+        },
+        "messages": [
+            {"role": "system", "text": "You are a smart kitchen agent. Suggest healthy and delicious recipes."},
+            {"role": "user", "text": prompt}
+        ]
     }
-    response = requests.post(
-        "https://llm.api.cloud.yandex.net/foundationModels/v1/completion",
-        headers=headers, json=data
-    )
+
+    response = requests.post(url, headers=headers, json=data)
+    
     try:
-        text = response.json()["result"]["alternatives"][0]["message"]["text"]
+        result = response.json()
+        return result["result"]["alternatives"][0]["message"]["text"]
     except Exception as e:
-        return f"❌ Error: Could not fetch recipe. {e}"
-    return text
-
-def extract_nutrition(recipe_text):
-    # More robust pattern allowing newlines and flexible spacing
-    pattern = (
-        r"Calories:\s*([\d\.]+)\s*kcal.*?"
-        r"Protein:\s*([\d\.]+)\s*g.*?"
-        r"Carbs:\s*([\d\.]+)\s*g.*?"
-        r"Fat:\s*([\d\.]+)\s*g"
-    )
-    match = re.search(pattern, recipe_text.replace("\n", " "))
-    if match:
-        calories, protein, carbs, fat = map(float, match.groups())
-        return {"Calories": calories, "Protein": protein, "Carbs": carbs, "Fat": fat}
-    return None
-
-def plot_nutrition_chart(nutrition):
-    fig, ax = plt.subplots()
-    nutrients = list(nutrition.keys())
-    values = list(nutrition.values())
-    colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']
-    ax.pie(values, labels=nutrients, autopct='%1.1f%%', startangle=140, colors=colors)
-    ax.axis('equal')
-    plt.tight_layout()
-
-    temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    plt.savefig(temp_file.name)
-    plt.close(fig)
-    return temp_file.name
+        return f"❌ Error: {e}\n\nResponse: {response.text}"
 
-def handle_generate(preferences, ingredients, time):
-    recipe = generate_meal(preferences, ingredients, time)
-    nutrition = extract_nutrition(recipe)
-    chart_path = None
-    if nutrition:
-        chart_path = plot_nutrition_chart(nutrition)
-    return recipe, chart_path
+# Gradio UI logic
+def generate_recipe(ingredients, preference):
+    if not ingredients:
+        return "⚠️ Please enter some ingredients."
+    prompt = f"Give me a recipe using the following ingredients: {ingredients}. Make it {preference.lower()} and healthy."
+    return call_nebius(prompt)
 
-def save_pdf(recipe_text):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-    for line in recipe_text.split('\n'):
-        pdf.multi_cell(0, 10, line)
-    temp_file = tempfile.NamedTemporaryFile(suffix=".pdf", delete=False)
-    pdf.output(temp_file.name)
-    return temp_file.name
-
-# Optional: placeholder function for speech-to-text (you can replace with real STT)
-def dummy_speech_to_text(audio):
-    # This just returns a fixed string for demo. Replace with actual STT model call if you want.
-    if audio is None:
-        return ""
-    return "tomato, onion, garlic"
-
-with gr.Blocks(css="""
-    body { background-color: #111; color: #eee; font-family: 'Segoe UI', sans-serif; }
-    .gradio-container { max-width: 900px; margin: auto; }
-    .input-label { font-weight: 600; margin-bottom: 5px; }
-    .footer { font-size: 0.8rem; color: #666; padding: 10px; text-align: center; }
-    @media (max-width: 600px) {
-        .gradio-container { padding: 10px; }
-    }
-""") as demo:
-    gr.Markdown("# 👨‍🍳 AgentChef: AI Recipe Planner & Smart Kitchen Assistant")
+# UI layout
+with gr.Blocks(theme=gr.themes.Base()) as demo:
+    gr.Markdown("👨‍🍳 **AgentChef: AI Recipe Planner & Smart Kitchen Assistant**")
 
     with gr.Row():
-        with gr.Column(scale=1, min_width=200):
-            preferences = gr.Textbox(label="🥗 Dietary Preferences", placeholder="e.g. vegetarian, keto")
-            ingredients = gr.Textbox(label="🧂 Ingredients You Have", placeholder="e.g. rice, tomato, onion", lines=3)
-            
-            mic = gr.Audio(type="filepath", label="🎤 Speak Ingredients")
-
-            transcribed = gr.Textbox(label="📝 Transcribed Ingredients (from mic)", interactive=False)
-
-            mic.change(dummy_speech_to_text, inputs=mic, outputs=transcribed)
-            # Add a button to copy transcribed text to ingredients textbox if needed
-            copy_btn = gr.Button("Copy Transcription to Ingredients")
-            copy_btn.click(lambda txt: txt, inputs=transcribed, outputs=ingredients)
-            
-            time = gr.Slider(5, 60, value=20, step=5, label="⏱️ Time Available (minutes)")
-            generate_btn = gr.Button("🍽️ Generate Recipe")
-
-        with gr.Column(scale=1, min_width=300):
-            recipe_output = gr.Textbox(label="📝 Recipe Output", lines=15, interactive=False)
-            nutrition_chart = gr.Image(label="📊 Nutrition Breakdown", interactive=False)
-            pdf_btn = gr.Button("📄 Export as PDF")
+        ingredients_input = gr.Textbox(label="🛒 Ingredients (comma-separated)", placeholder="e.g. tomato, onion, rice")
+        preference_input = gr.Dropdown(label="🥗 Dietary Preferences", choices=["Vegetarian", "Vegan", "Keto", "No Preference"], value="Vegetarian")
 
-    generate_btn.click(handle_generate,
-                       inputs=[preferences, ingredients, time],
-                       outputs=[recipe_output, nutrition_chart])
+    submit_btn = gr.Button("🍽️ Generate Recipe")
+    output = gr.Textbox(label="📋 Recipe Result", lines=10)
 
-    pdf_btn.click(save_pdf, inputs=[recipe_output], outputs=gr.File(label="📥 Download Recipe PDF"))
+    submit_btn.click(generate_recipe, inputs=[ingredients_input, preference_input], outputs=output)
 
-demo.launch()
+# Launch
+demo.launch()