Update app.py

app.py

@@ -1,92 +1,69 @@
-# RPA Business Analyst AI Agent Implementation
-# pip install pm4py neo4j rasa opencv-python pytesseract tensorflow scikit-learn
+from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
+import datetime
+import requests
+import pytz
+import yaml
+from tools.final_answer import FinalAnswerTool
 
-# imports
-import pm4py
-import cv2
-from mss import mss
-import pytesseract
-import spacy
-from neo4j import GraphDatabase
-import numpy as np
-import pandas as pd
-import json
-import tensorflow as tf
-from sklearn.ensemble import GradientBoostingRegressor
+from Gradio_UI import GradioUI
 
-# Process Mining Module
-def discover_process(event_log_path, algorithm="inductive"):
-    log = pm4py.read_xes(event_log_path)
-    if algorithm == "inductive":
-        model = pm4py.discover_petri_net_inductive(log)
-    else:
-        model = pm4py.discover_petri_net_alpha(log)
-    pm4py.view_petri_net(*model)
-    return model
+# 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 
+    Args:
+        arg1: the first argument
+        arg2: the second argument
+    """
+    return "What magic will you build ?"
 
-# Computer Vision Module
-class ScreenCapture:
-    def capture_screen(self):
-        with mss() as sct:
-            monitor = {"top": 0, "left": 0, "width": 1920, "height": 1080}
-            img = sct.grab(monitor)
-            return cv2.cvtColor(np.array(img), cv2.COLOR_BGRA2BGR)
-    
-    def extract_text(self, image):
-        return pytesseract.image_to_string(image)
+@tool
+def get_current_time_in_timezone(timezone: str) -> str:
+    """A tool that fetches the current local time in a specified timezone.
+    Args:
+        timezone: A string representing a valid timezone (e.g., 'America/New_York').
+    """
+    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}"
+    except Exception as e:
+        return f"Error fetching time for timezone '{timezone}': {str(e)}"
 
-# NLP Module
-nlp_model = spacy.load("en_core_web_sm")
-def parse_workflows(text):
-    doc = nlp_model(text)
-    return [ent.text for ent in doc.ents if ent.label_ == "WORKFLOW"]
 
-# Requirements Analysis Module
-driver = GraphDatabase.driver("bolt://localhost", auth=None)
-def create_process_node(process_name, process_description):
-    with driver.session() as session:
-        session.run(
-            "MERGE (p:Process {name: $name}) SET p.description = $description",
-            name=process_name,
-            description=process_description
-        )
+final_answer = FinalAnswerTool()
 
-# Automation Opportunity Detector
-class SuitabilityPredictor:
-    def __init__(self, model_path):
-        self.model = tf.keras.models.load_model(model_path)
-    
-    def predict(self, features):
-        return self.model.predict(features)[0][0]
+# 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' 
 
-def automate_suitability(process_data, model_path):
-    predictor = SuitabilityPredictor(model_path)
-    return predictor.predict(process_data)
+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,
+)
 
-# Main Execution Block
-if __name__ == "__main__":
-    # Load Configuration
-    with open("config.json") as f:
-        config = json.load(f)
-    
-    # Process Mining
-    event_log_path = "data/event_logs.xes"
-    process_model = discover_process(event_log_path, config["process_mining"]["algorithm"])
-    
-    # Computer Vision
-    screen_cap = ScreenCapture()
-    screen_img = screen_cap.capture_screen()
-    screen_text = screen_cap.extract_text(screen_img)
-    
-    # NLP
-    workflows = parse_workflows(screen_text)
-    
-    # Knowledge Graph
-    create_process_node("Invoice Processing", "Processes vendor invoices")
-    
-    # ML Suitability
-    model_path = config["ml"]["model_path"]
-    suitability_score = automate_suitability(process_data, model_path)
+
+# Import tool from Hub
+image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
+
+with open("prompts.yaml", 'r') as stream:
+    prompt_templates = yaml.safe_load(stream)
     
-    # Recommendation Engine
-    print(f"Automation Suitability: {suitability_score:.2f}")
+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()