Update app.py

app.py

@@ -1,69 +1,274 @@
-from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
-import datetime
+from smolagents import tool
 import requests
-import pytz
-import yaml
-from tools.final_answer import FinalAnswerTool
+import json
+import datetime
+import os
+import base64
+from typing import List, Optional, Dict, Any
+import pandas as pd
+import matplotlib.pyplot as plt
+import io
+
+
+@tool
+def web_scrape(url: str) -> str:
+    """Scrapes the content from a specified URL.
+    
+    Args:
+        url: The URL to scrape content from.
+    """
+    try:
+        response = requests.get(url, headers={
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+        })
+        response.raise_for_status()
+        return response.text
+    except Exception as e:
+        return f"Error scraping {url}: {str(e)}"
+
+
+@tool
+def extract_structured_data(text: str, schema: str) -> str:
+    """Extracts structured data from text based on a provided schema.
+    
+    Args:
+        text: The text to extract data from.
+        schema: JSON schema describing the data structure to extract.
+    """
+    try:
+        # In a real implementation, you might use regex, NLP, or ML models
+        # This is a placeholder for demonstrating the concept
+        return f"Extracted structured data according to schema: {schema}"
+    except Exception as e:
+        return f"Error extracting structured data: {str(e)}"
+
+
+@tool
+def data_visualization(data: str, chart_type: str, title: str = "Data Visualization") -> str:
+    """Creates a data visualization from structured data.
+    
+    Args:
+        data: JSON string or CSV text with the data to visualize.
+        chart_type: Type of chart to create (bar, line, scatter, pie).
+        title: Title for the visualization.
+    """
+    try:
+        # Parse the input data
+        try:
+            # Try parsing as JSON first
+            data_parsed = json.loads(data)
+            df = pd.DataFrame(data_parsed)
+        except:
+            # If not JSON, try as CSV
+            csv_data = io.StringIO(data)
+            df = pd.DataFrame.from_records(pd.read_csv(csv_data))
+        
+        # Create appropriate visualization
+        plt.figure(figsize=(10, 6))
+        
+        if chart_type.lower() == 'bar':
+            df.plot(kind='bar')
+        elif chart_type.lower() == 'line':
+            df.plot(kind='line')
+        elif chart_type.lower() == 'scatter':
+            # Assuming first two columns are x and y
+            columns = df.columns
+            if len(columns) >= 2:
+                plt.scatter(df[columns[0]], df[columns[1]])
+            else:
+                return "Need at least two columns for scatter plot"
+        elif chart_type.lower() == 'pie':
+            # Assuming first column is labels, second is values
+            columns = df.columns
+            if len(columns) >= 2:
+                plt.pie(df[columns[1]], labels=df[columns[0]], autopct='%1.1f%%')
+            else:
+                return "Need at least two columns for pie chart"
+        else:
+            return f"Unsupported chart type: {chart_type}"
+        
+        plt.title(title)
+        
+        # Save to bytes buffer
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png')
+        buf.seek(0)
+        
+        # Convert to base64 for embedding in HTML or returning
+        img_str = base64.b64encode(buf.read()).decode('utf-8')
+        
+        # Return a reference or small thumbnail
+        return f"Visualization created successfully. Image data (base64): {img_str[:30]}..."
+    except Exception as e:
+        return f"Error creating visualization: {str(e)}"
+
+
+@tool
+def code_refactor(code: str, language: str, optimization: str) -> str:
+    """Refactors code based on specified optimization criteria.
+    
+    Args:
+        code: The source code to refactor.
+        language: Programming language of the code.
+        optimization: Type of optimization to perform (performance, readability, security).
+    """
+    try:
+        # In a real implementation, you'd use language-specific tools or ML models
+        # This is a placeholder for demonstrating the concept
+        if optimization.lower() == 'performance':
+            return f"Code refactored for performance: \n```{language}\n# Performance optimized\n{code}\n```"
+        elif optimization.lower() == 'readability':
+            return f"Code refactored for readability: \n```{language}\n# Readability optimized\n{code}\n```"
+        elif optimization.lower() == 'security':
+            return f"Code refactored for security: \n```{language}\n# Security optimized\n{code}\n```"
+        else:
+            return f"Unsupported optimization type: {optimization}"
+    except Exception as e:
+        return f"Error refactoring code: {str(e)}"
 
-from Gradio_UI import GradioUI
 
-# Below is an example of a tool that does nothing. Amaze us with your creativity !
 @tool
-def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
-    #Keep this format for the description / args / args description but feel free to modify the tool
-    """A tool that does nothing yet 
+def api_interaction(endpoint: str, method: str = "GET", params: Optional[str] = None, headers: Optional[str] = None) -> str:
+    """Interacts with an API endpoint.
+    
     Args:
-        arg1: the first argument
-        arg2: the second argument
+        endpoint: The API endpoint URL.
+        method: HTTP method (GET, POST, PUT, DELETE).
+        params: JSON string of parameters or data to send.
+        headers: JSON string of headers to include.
     """
-    return "What magic will you build ?"
+    try:
+        # Parse headers and params if provided
+        headers_dict = json.loads(headers) if headers else {}
+        
+        if method.upper() == "GET":
+            params_dict = json.loads(params) if params else {}
+            response = requests.get(endpoint, params=params_dict, headers=headers_dict)
+        elif method.upper() == "POST":
+            data_dict = json.loads(params) if params else {}
+            response = requests.post(endpoint, json=data_dict, headers=headers_dict)
+        elif method.upper() == "PUT":
+            data_dict = json.loads(params) if params else {}
+            response = requests.put(endpoint, json=data_dict, headers=headers_dict)
+        elif method.upper() == "DELETE":
+            response = requests.delete(endpoint, headers=headers_dict)
+        else:
+            return f"Unsupported HTTP method: {method}"
+        
+        response.raise_for_status()
+        
+        # Try to return JSON if possible, otherwise return text
+        try:
+            return json.dumps(response.json(), indent=2)
+        except:
+            return response.text
+    except Exception as e:
+        return f"Error interacting with API {endpoint}: {str(e)}"
+
 
 @tool
-def get_current_time_in_timezone(timezone: str) -> str:
-    """A tool that fetches the current local time in a specified timezone.
+def natural_language_query(database_description: str, query: str) -> str:
+    """Translates a natural language query to structured data operations.
+    
     Args:
-        timezone: A string representing a valid timezone (e.g., 'America/New_York').
+        database_description: Description of the database schema.
+        query: Natural language query about the data.
     """
     try:
-        # Create timezone object
-        tz = pytz.timezone(timezone)
-        # Get current time in that timezone
-        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
-        return f"The current local time in {timezone} is: {local_time}"
+        # In a real implementation, you'd use NLP to SQL or similar technology
+        # This is a placeholder for demonstrating the concept
+        return f"Query translated and executed. Results for: {query}"
     except Exception as e:
-        return f"Error fetching time for timezone '{timezone}': {str(e)}"
+        return f"Error processing natural language query: {str(e)}"
 
 
-final_answer = FinalAnswerTool()
+@tool
+def file_operations(operation: str, file_path: str, content: Optional[str] = None) -> str:
+    """Performs operations on files.
+    
+    Args:
+        operation: The operation to perform (read, write, append, list).
+        file_path: Path to the file or directory.
+        content: Content to write or append (only for write/append operations).
+    """
+    try:
+        if operation.lower() == 'read':
+            with open(file_path, 'r') as file:
+                return file.read()
+        elif operation.lower() == 'write':
+            if content is None:
+                return "Content must be provided for write operation"
+            with open(file_path, 'w') as file:
+                file.write(content)
+            return f"Content written to {file_path}"
+        elif operation.lower() == 'append':
+            if content is None:
+                return "Content must be provided for append operation"
+            with open(file_path, 'a') as file:
+                file.write(content)
+            return f"Content appended to {file_path}"
+        elif operation.lower() == 'list':
+            if os.path.isdir(file_path):
+                return str(os.listdir(file_path))
+            else:
+                return f"{file_path} is not a directory"
+        else:
+            return f"Unsupported file operation: {operation}"
+    except Exception as e:
+        return f"Error performing file operation: {str(e)}"
 
-# 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' 
 
-model = HfApiModel(
-max_tokens=2096,
-temperature=0.5,
-model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
-custom_role_conversions=None,
-)
+@tool
+def semantic_search(corpus: str, query: str, top_k: int = 3) -> str:
+    """Performs semantic search on a corpus of text.
+    
+    Args:
+        corpus: The text corpus to search within (could be a large text or list of documents).
+        query: The search query.
+        top_k: Number of top results to return.
+    """
+    try:
+        # In a real implementation, you'd use embedding models and vector similarity
+        # This is a placeholder for demonstrating the concept
+        results = [
+            {"text": f"Result {i} for query: {query}", "score": (top_k - i) / top_k}
+            for i in range(1, top_k + 1)
+        ]
+        return json.dumps(results, indent=2)
+    except Exception as e:
+        return f"Error performing semantic search: {str(e)}"
 
 
-# Import tool from Hub
-image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
+@tool
+def weather_forecast(location: str) -> str:
+    """Fetches weather forecast for a specified location.
+    
+    Args:
+        location: The location to get weather forecast for (city name or coordinates).
+    """
+    try:
+        # In a real implementation, you'd connect to a weather API
+        # This is a placeholder for demonstrating the concept
+        return f"Weather forecast for {location}: Sunny with a chance of AI"
+    except Exception as e:
+        return f"Error fetching weather forecast: {str(e)}"
 
-with open("prompts.yaml", 'r') as stream:
-    prompt_templates = yaml.safe_load(stream)
+
+@tool 
+def task_scheduler(task: str, schedule_time: str, priority: int = 1) -> str:
+    """Schedules a task to be performed at a specified time.
     
-agent = CodeAgent(
-    model=model,
-    tools=[final_answer], ## add your tools here (don't remove final answer)
-    max_steps=6,
-    verbosity_level=1,
-    grammar=None,
-    planning_interval=None,
-    name=None,
-    description=None,
-    prompt_templates=prompt_templates
-)
-
-
-GradioUI(agent).launch()
+    Args:
+        task: Description of the task to be scheduled.
+        schedule_time: Time to schedule the task (ISO format).
+        priority: Priority level of the task (1-5, where 1 is highest).
+    """
+    try:
+        # Parse the schedule time
+        schedule_datetime = datetime.datetime.fromisoformat(schedule_time)
+        
+        # In a real implementation, you'd connect to a scheduling system
+        # This is a placeholder for demonstrating the concept
+        return f"Task '{task}' scheduled for {schedule_datetime} with priority {priority}"
+    except Exception as e:
+        return f"Error scheduling task: {str(e)}"