Update app.py

app.py

@@ -1,14 +1,9 @@
-# MCP-Powered Voice Assistant with Open-Source Tools
-# Hugging Face Space Implementation
-
 import gradio as gr
 import numpy as np
 import sqlite3
 import json
-import requests
-from PIL import Image
-import io
 import time
+from PIL import Image, ImageDraw
 
 # ------ Mock MCP Server Implementation ------
 class MockMCPServer:
@@ -33,6 +28,7 @@ mcp_server = MockMCPServer()
 def get_recipe_by_ingredients(ingredients):
     """Find recipes based on available ingredients"""
     # In a real implementation, this would call an API
+    print(f"Searching recipes with ingredients: {ingredients}")
     return {
         "recipes": [
             {"name": "Vegetable Stir Fry", "time": 20, "difficulty": "Easy"},
@@ -42,14 +38,16 @@ def get_recipe_by_ingredients(ingredients):
 
 def get_recipe_image(recipe_name):
     """Generate an image of the finished recipe"""
-    # In production, this would call a model like Stable Diffusion
-    return {
-        "image_url": "https://example.com/recipe-image.jpg",
-        "alt_text": f"Image of {recipe_name}"
-    }
+    print(f"Generating image for: {recipe_name}")
+    # Create a placeholder image with the recipe name
+    img = Image.new('RGB', (300, 200), color=(73, 109, 137))
+    d = ImageDraw.Draw(img)
+    d.text((10,10), f"Image of: {recipe_name}", fill=(255,255,0))
+    return img
 
 def convert_measurements(amount, from_unit, to_unit):
     """Convert cooking measurements between units"""
+    print(f"Converting {amount} {from_unit} to {to_unit}")
     conversions = {
         ("tbsp", "tsp"): lambda x: x * 3,
         ("cups", "ml"): lambda x: x * 240,
@@ -57,7 +55,8 @@ def convert_measurements(amount, from_unit, to_unit):
     }
     conversion_key = (from_unit.lower(), to_unit.lower())
     if conversion_key in conversions:
-        return {"result": conversions[conversion_key](amount), "unit": to_unit}
+        result = conversions[conversion_key](amount)
+        return {"result": round(result, 2), "unit": to_unit}
     return {"error": "Conversion not supported"}
 
 # ------ Recipe Database ------
@@ -71,7 +70,9 @@ def init_recipe_db():
         ("Classic Pancakes", json.dumps(["flour", "eggs", "milk", "baking powder"]), 
          "1. Mix dry ingredients\n2. Add wet ingredients\n3. Cook on griddle", 15),
         ("Tomato Soup", json.dumps(["tomatoes", "onion", "garlic", "vegetable stock"]), 
-         "1. Sauté onions\n2. Add tomatoes\n3. Simmer and blend", 30)
+         "1. Sauté onions\n2. Add tomatoes\n3. Simmer and blend", 30),
+        ("Chocolate Cake", json.dumps(["flour", "sugar", "cocoa", "eggs", "milk"]), 
+         "1. Mix dry ingredients\n2. Add wet ingredients\n3. Bake at 350°F", 45)
     ]
     
     c.executemany("INSERT INTO recipes (name, ingredients, instructions, prep_time) VALUES (?,?,?,?)", recipes)
@@ -82,42 +83,73 @@ def init_recipe_db():
 def text_to_speech(text):
     """Mock TTS function - in real use, replace with actual TTS"""
     print(f"[TTS]: {text}")
-    # Return dummy audio data
-    return np.zeros(16000, dtype=np.float32), 16000
+    # Return dummy audio data (silence)
+    duration = 2  # seconds
+    sample_rate = 44100
+    samples = np.zeros(int(duration * sample_rate), dtype=np.float32)
+    return (sample_rate, samples)
 
 def speech_to_text(audio):
     """Mock STT function - in real use, replace with actual STT"""
-    # Return dummy text
-    return "Show me pancake recipes"
+    # For now, we return a fixed string. In reality, we would process the audio
+    sample_rate, audio_data = audio
+    print(f"Received audio with sample rate {sample_rate} and shape {audio_data.shape}")
+    # Return a fixed response for demo
+    return "What can I make with eggs and flour?"
 
 # ------ Agent Logic ------
 def process_query(query, db_conn):
     """Process user query using the available tools"""
+    print(f"Processing query: {query}")
     # Simple intent recognition
-    if "recipe" in query.lower() or "make" in query.lower():
-        # Extract ingredients
-        ingredients = ["flour", "eggs"]  # Simplified extraction
-        return mcp_server.call_tool(
-            "get_recipe_by_ingredients",
-            {"ingredients": ingredients}
-        )
-    elif "image" in query.lower() or "show" in query.lower():
-        recipe_name = "Classic Pancakes"  # Simplified extraction
-        return mcp_server.call_tool(
-            "get_recipe_image",
-            {"recipe_name": recipe_name}
-        )
+    if "recipe" in query.lower() or "make" in query.lower() or "cook" in query.lower():
+        # Extract ingredients - very simple, just use some keywords
+        ingredients = []
+        for word in ["eggs", "flour", "milk", "tomatoes", "onion", "garlic"]:
+            if word in query.lower():
+                ingredients.append(word)
+        if not ingredients:
+            ingredients = ["eggs", "flour"]  # default
+        return {
+            "type": "recipes",
+            "data": mcp_server.call_tool("get_recipe_by_ingredients", {"ingredients": ingredients})
+        }
+    elif "image" in query.lower() or "show" in query.lower() or "look" in query.lower():
+        # Extract recipe name
+        recipe_name = "Classic Pancakes"  # default
+        for recipe in ["pancakes", "stir fry", "tomato soup", "chocolate cake"]:
+            if recipe in query.lower():
+                recipe_name = recipe
+                break
+        return {
+            "type": "image",
+            "data": mcp_server.call_tool("get_recipe_image", {"recipe_name": recipe_name})
+        }
     elif "convert" in query.lower():
-        # Simplified extraction
-        return mcp_server.call_tool(
-            "convert_measurements",
-            {"amount": 2, "from_unit": "cups", "to_unit": "ml"}
-        )
+        # Extract amount and units - very simple
+        # Assume pattern: convert <number> <unit> to <unit>
+        words = query.split()
+        try:
+            amount = float(words[words.index("convert")+1])
+            from_unit = words[words.index("convert")+2]
+            to_unit = words[words.index("to")+1]
+        except:
+            amount = 2
+            from_unit = "cups"
+            to_unit = "ml"
+        return {
+            "type": "conversion",
+            "data": mcp_server.call_tool("convert_measurements", {"amount": amount, "from_unit": from_unit, "to_unit": to_unit})
+        }
     else:
         # Fallback to database search
         c = db_conn.cursor()
         c.execute("SELECT * FROM recipes WHERE name LIKE ?", (f"%{query}%",))
-        return c.fetchall()
+        recipes = c.fetchall()
+        return {
+            "type": "db_recipes",
+            "data": recipes
+        }
 
 # ------ Register Tools with MCP Server ------
 mcp_server.register_tool(
@@ -148,48 +180,57 @@ def process_voice_command(audio):
     # Process query using agent logic
     result = process_query(query, db_conn)
     
-    # Generate response text
-    if isinstance(result, list) and result:
-        response_text = f"Found {len(result)} recipes:\n"
-        for item in result:
-            response_text += f"- {item[1]} ({item[4]} mins)\n"
-    elif "recipes" in result:
-        response_text = f"Found {len(result['recipes'])} recipes:\n"
-        for recipe in result["recipes"]:
-            response_text += f"- {recipe['name']} ({recipe['time']} mins)\n"
-    elif "image_url" in result:
-        response_text = f"Here's an image of {result.get('alt_text', 'the recipe')}"
-        image = Image.new('RGB', (300, 200), color=(73, 109, 137))
+    # Generate response text and image
+    response_text = ""
+    image = None
+    
+    if result["type"] == "recipes":
+        recipes = result["data"]["recipes"]
+        response_text = f"Found {len(recipes)} recipes:\n"
+        for recipe in recipes:
+            response_text += f"- {recipe['name']} ({recipe['time']} mins, {recipe['difficulty']})\n"
+    elif result["type"] == "image":
+        image = result["data"]  # This is a PIL image
+        response_text = "Here is an image of the recipe!"
+    elif result["type"] == "conversion":
+        conv = result["data"]
+        if "error" in conv:
+            response_text = f"Error: {conv['error']}"
+        else:
+            response_text = f"{conv['result']} {conv['unit']}"
+    elif result["type"] == "db_recipes":
+        recipes = result["data"]
+        if recipes:
+            response_text = f"Found {len(recipes)} recipes in database:\n"
+            for recipe in recipes:
+                response_text += f"- {recipe[1]} ({recipe[4]} mins)\n"
+        else:
+            response_text = "No recipes found."
     else:
-        response_text = str(result)
-        image = None
+        response_text = "I'm not sure how to help with that."
     
     # Convert response to audio
-    audio_data, sr = text_to_speech(response_text)
+    sr, audio_data = text_to_speech(response_text)
     
-    # Return results
-    return (
-        (sr, audio_data),
-        response_text,
-        image if 'image' in locals() else None
-    )
+    # Return results: audio output, text, and image
+    return (sr, audio_data), response_text, image
 
 # ------ Hugging Face Space UI ------
 with gr.Blocks(title="MCP Culinary Voice Assistant") as demo:
-    gr.Markdown("# 🧑‍🍳 MCP-Powered Culinary Voice Assistant (Open-Source)")
+    gr.Markdown("# 🧑‍🍳 MCP-Powered Culinary Voice Assistant")
     gr.Markdown("Speak to your cooking assistant about recipes, conversions, and more!")
     
     with gr.Row():
-        audio_input = gr.Audio(source="microphone", type="numpy", label="Speak to Chef Assistant")
-        audio_output = gr.Audio(label="Assistant Response", interactive=False)
+        with gr.Column():
+            audio_input = gr.Audio(source="microphone", type="numpy", label="Speak to Chef Assistant")
+            submit_btn = gr.Button("Process Command", variant="primary")
+        with gr.Column():
+            audio_output = gr.Audio(label="Assistant Response", interactive=False)
     
     with gr.Row():
         text_output = gr.Textbox(label="Transcription", interactive=False)
         image_output = gr.Image(label="Recipe Image", interactive=False)
     
-    with gr.Row():
-        submit_btn = gr.Button("Process Command", variant="primary")
-    
     submit_btn.click(
         fn=process_voice_command,
         inputs=[audio_input],
@@ -200,7 +241,8 @@ with gr.Blocks(title="MCP Culinary Voice Assistant") as demo:
         examples=[
             ["What can I make with eggs and flour?"],
             ["Show me how tomato soup looks"],
-            ["Convert 2 cups to milliliters"]
+            ["Convert 2 cups to milliliters"],
+            ["Find chocolate cake recipes"]
         ],
         inputs=[text_output],
         label="Example Queries"