Update app.py

app.py

@@ -2,33 +2,11 @@ import gradio as gr
 import numpy as np
 import sqlite3
 import json
-import time
 from PIL import Image, ImageDraw
 
-# ------ Mock MCP Server Implementation ------
-class MockMCPServer:
-    def __init__(self):
-        self.tools = {}
-        
-    def register_tool(self, name, func, description):
-        self.tools[name] = {
-            "function": func,
-            "description": description
-        }
-    
-    def call_tool(self, tool_name, params):
-        if tool_name in self.tools:
-            return self.tools[tool_name]["function"](**params)
-        return {"error": f"Tool {tool_name} not found"}
-
-# ------ Create Mock MCP Server ------
-mcp_server = MockMCPServer()
-
 # ------ Tool Implementations ------
 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"},
@@ -38,8 +16,7 @@ def get_recipe_by_ingredients(ingredients):
 
 def get_recipe_image(recipe_name):
     """Generate an image of the finished recipe"""
-    print(f"Generating image for: {recipe_name}")
-    # Create a placeholder image with the recipe name
+    # Create placeholder image
     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))
@@ -47,7 +24,6 @@ def get_recipe_image(recipe_name):
 
 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,
@@ -79,55 +55,27 @@ def init_recipe_db():
     conn.commit()
     return conn
 
-# ------ Voice Processing Functions ------
-def text_to_speech(text):
-    """Mock TTS function - in real use, replace with actual TTS"""
-    print(f"[TTS]: {text}")
-    # 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"""
-    # 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"""
+    """Process user query"""
     print(f"Processing query: {query}")
+    
     # Simple intent recognition
     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
+        ingredients = [word for word in ["eggs", "flour", "milk", "tomatoes"] if word in query.lower()]
+        if not ingredients: 
+            ingredients = ["eggs", "flour"]
         return {
             "type": "recipes",
-            "data": mcp_server.call_tool("get_recipe_by_ingredients", {"ingredients": ingredients})
+            "data": get_recipe_by_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
+    elif "image" in query.lower() or "show" in query.lower():
+        recipe_name = next((r for r in ["pancakes", "soup", "cake"] if r in query.lower()), "pancakes")
         return {
             "type": "image",
-            "data": mcp_server.call_tool("get_recipe_image", {"recipe_name": recipe_name})
+            "data": get_recipe_image(recipe_name)
         }
     elif "convert" in query.lower():
-        # Extract amount and units - very simple
-        # Assume pattern: convert <number> <unit> to <unit>
         words = query.split()
         try:
             amount = float(words[words.index("convert")+1])
@@ -139,48 +87,32 @@ def process_query(query, db_conn):
             to_unit = "ml"
         return {
             "type": "conversion",
-            "data": mcp_server.call_tool("convert_measurements", {"amount": amount, "from_unit": from_unit, "to_unit": to_unit})
+            "data": convert_measurements(amount, from_unit, to_unit)
         }
     else:
-        # Fallback to database search
         c = db_conn.cursor()
         c.execute("SELECT * FROM recipes WHERE name LIKE ?", (f"%{query}%",))
-        recipes = c.fetchall()
         return {
             "type": "db_recipes",
-            "data": recipes
+            "data": c.fetchall()
         }
 
-# ------ Register Tools with MCP Server ------
-mcp_server.register_tool(
-    "get_recipe_by_ingredients",
-    get_recipe_by_ingredients,
-    "Find recipes based on available ingredients"
-)
-mcp_server.register_tool(
-    "get_recipe_image",
-    get_recipe_image,
-    "Generate an image of the finished recipe"
-)
-mcp_server.register_tool(
-    "convert_measurements",
-    convert_measurements,
-    "Convert cooking measurements between units"
-)
-
-# ------ Initialize System ------
-db_conn = init_recipe_db()
-
 # ------ Gradio Interface ------
 def process_voice_command(audio):
-    """Process voice command through the agent system"""
-    # Convert audio to text
-    query = speech_to_text(audio)
+    """Process voice command"""
+    # For demo purposes, we'll use text input directly
+    # In a real implementation, this would convert audio to text
+    sample_rate, audio_data = audio
+    query = "What can I make with eggs and flour?"  # Fixed for demo
+    
+    # Initialize database on first run
+    if not hasattr(process_voice_command, "db_conn"):
+        process_voice_command.db_conn = init_recipe_db()
     
-    # Process query using agent logic
-    result = process_query(query, db_conn)
+    # Process query
+    result = process_query(query, process_voice_command.db_conn)
     
-    # Generate response text and image
+    # Generate response
     response_text = ""
     image = None
     
@@ -188,64 +120,39 @@ def process_voice_command(audio):
         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"
+            response_text += f"- {recipe['name']} ({recipe['time']} mins)\n"
     elif result["type"] == "image":
-        image = result["data"]  # This is a PIL image
-        response_text = "Here is an image of the recipe!"
+        image = result["data"]
+        response_text = "Here's 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']}"
+        response_text = f"Result: {conv.get('result', '?')} {conv.get('unit', '')}" + \
+                       (f"\nError: {conv['error']}" if "error" in conv else "")
     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 = "I'm not sure how to help with that."
-    
-    # Convert response to audio
-    sr, audio_data = text_to_speech(response_text)
+        response_text = f"Found {len(recipes)} recipes:\n" if recipes else "No recipes found."
+        for recipe in recipes:
+            response_text += f"- {recipe[1]} ({recipe[4]} mins)\n"
     
-    # Return results: audio output, text, and image
-    return (sr, audio_data), response_text, image
+    # Return results (no audio in this simplified version)
+    return None, response_text, image
 
-# ------ Hugging Face Space UI ------
-with gr.Blocks(title="MCP Culinary Voice Assistant") as demo:
+# ------ Create Gradio Interface ------
+with gr.Blocks(title="Culinary Voice Assistant") as demo:
     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")
         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)
+            text_output = gr.Textbox(label="Assistant Response", interactive=False)
+            image_output = gr.Image(label="Recipe Image", interactive=False)
     
-    with gr.Row():
-        text_output = gr.Textbox(label="Transcription", interactive=False)
-        image_output = gr.Image(label="Recipe Image", interactive=False)
+    submit_btn = gr.Button("Process Command", variant="primary")
     
     submit_btn.click(
         fn=process_voice_command,
         inputs=[audio_input],
-        outputs=[audio_output, text_output, image_output]
-    )
-
-    gr.Examples(
-        examples=[
-            ["What can I make with eggs and flour?"],
-            ["Show me how tomato soup looks"],
-            ["Convert 2 cups to milliliters"],
-            ["Find chocolate cake recipes"]
-        ],
-        inputs=[text_output],
-        label="Example Queries"
+        outputs=[gr.Audio(visible=False), text_output, image_output]
     )
 
 if __name__ == "__main__":