Update app.py

app.py

@@ -11,81 +11,31 @@ from bs4 import BeautifulSoup
 from typing import List, Dict, Any, Optional
 from Gradio_UI import GradioUI
 
-
-@tool
-def extract_recipe_details(url: str) -> Dict[str, Any]:
-    """Extract recipe details including ingredients, instructions, and nutrition from a webpage.
-    
-    url:
-        The complete URL of the recipe webpage to scrape.
-    
-    Returns:
-        A dictionary containing ingredients list, cooking instructions, and nutritional information.
+def get_healthy_cheat_meal(diet_type: str) -> List[Dict[str, Any]]:
     """
-    try:
-        headers = {"User-Agent": "Mozilla/5.0"}
-        response = requests.get(url, headers=headers, timeout=10)
-        response.raise_for_status()
-
-        soup = BeautifulSoup(response.text, "html.parser")
-
-        # Extract ingredients
-        possible_ingredient_selectors = ["ul", "li", ".ingredients", ".recipe-ingredients", ".ingredient-list"]
-        ingredients = []
-        
-        for selector in possible_ingredient_selectors:
-            for tag in soup.select(selector):
-                items = [item.get_text(strip=True) for item in tag.find_all("li")]
-                if items:
-                    ingredients.extend(items)
-
-        # Extract instructions
-        possible_instruction_selectors = ["ol", "li", ".instructions", ".recipe-instructions", ".step", ".method"]
-        instructions = []
-        
-        for selector in possible_instruction_selectors:
-            for tag in soup.select(selector):
-                steps = [step.get_text(strip=True) for step in tag.find_all("li")]
-                if steps:
-                    instructions.extend(steps)
-
-        # Extract nutritional information (Look for tables or structured text)
-        possible_nutrition_selectors = [".nutrition", ".nutritional-info", ".nutrition-facts", "table"]
-        nutrition_info = []
-        
-        for selector in possible_nutrition_selectors:
-            for tag in soup.select(selector):
-                text = tag.get_text(strip=True)
-                if text and len(text) > 20:
-                    nutrition_info.append(text)
+    You are a smart culinary research assistant specializing in healthy alternatives for sweet dishes. 
+    Your role is to search the internet and compile healthy recipes and cooking methods that cater to specific diet types.
+    
+    When a user provides a diet type—such as Keto, Paleo, Vegan, Vegetarian, or Non-Vegetarian—retrieve and summarize 
+    reliable, up-to-date information on healthy sweet dish alternatives.
 
-        # Fallback if no nutrition data found
-        nutrition_text = nutrition_info[0] if nutrition_info else "Nutritional information not available. Check the source link."
+    Ensure that your responses include:
+    - Clear Recipe Details: List ingredients and nutritional benefits.
+    - Diet-Specific Adaptations: Highlight modifications and substitutions relevant to the specified diet type.
+    - Credible Sources: Rely on trusted culinary and nutritional resources to ensure accuracy and quality.
 
-        return {
-            "ingredients": ingredients if ingredients else ["Ingredients not found. Check the source link."],
-            "instructions": instructions if instructions else ["Instructions not found. Check the source link."],
-            "nutritional_benefits": nutrition_text
-        }
-    
-    except Exception as e:
-        return {
-            "ingredients": [f"Error extracting ingredients: {str(e)}"],
-            "instructions": [f"Error extracting instructions: {str(e)}"],
-            "nutritional_benefits": f"Error extracting nutritional info: {str(e)}"
-        }
+    Args:
+        diet_type (str): User-provided diet type.
 
-@tool
-def get_healthy_cheat_meal(diet_type: str) -> List[Dict[str, Any]]:
-    """Search and extract healthy sweet dish recipes based on diet type.
-    
-    diet_type:
-        The specific diet category (e.g., Keto, Paleo, Vegan, Vegetarian, Non-Vegetarian).
-    
     Returns:
-        A list of recipe dictionaries containing recipe details and instructions.
+        List[Dict[str, Any]]: A list of dictionaries with the following structure:
+            {
+                "recipe_name": str,             # The name of the recipe.
+                "source": str                   # A credible source or URL where the recipe was found.
+            }
     """
-    query = f"healthy {diet_type} sweet dish recipe with ingredients, instructions, and nutrition facts"
+    
+    query = f"healthy {diet_type} sweet dish recipe with ingredients and instructions"
     
     try:
         # Perform web search using DuckDuckGo
@@ -97,20 +47,9 @@ def get_healthy_cheat_meal(diet_type: str) -> List[Dict[str, Any]]:
         recipes = []
         
         for result in search_results:
-            url = result.get("href", "")
-            recipe_details = extract_recipe_details(url) if url else {
-                "ingredients": ["No source available."],
-                "instructions": ["No source available."],
-                "nutritional_benefits": "No source available."
-            }
-            
             recipes.append({
                 "recipe_name": result.get("title", "Unknown Recipe"),
-                "ingredients": recipe_details["ingredients"],
-                "nutritional_benefits": recipe_details["nutritional_benefits"],
-                "adaptations": f"This recipe follows a {diet_type} diet. Some modifications may be needed.",
-                "instructions": recipe_details["instructions"],
-                "source": url
+                "source": result.get("href", "No source available")
             })
         
         return recipes
@@ -118,9 +57,6 @@ def get_healthy_cheat_meal(diet_type: str) -> List[Dict[str, Any]]:
     except Exception as e:
         return [{"error": f"An error occurred while searching: {str(e)}"}]
 
-
-final_answer = FinalAnswerTool()
-
 # If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
 # model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'