Update app.py

app.py

@@ -1,101 +1,150 @@
-
-from flask import Flask, request, jsonify
-import requests
-import json
-
-app = Flask(__name__)
-
-class HuggingFace3DModels:
-    def __init__(self, api_token):
-        self.api_token = api_token
-        self.api_url = "https://api-inference.huggingface.co/models/"
-
-    def _request_model(self, model_name, inputs):
-        headers = {
-            "Authorization": f"Bearer {self.api_token}",
-            "Content-Type": "application/json"
-        }
-        response = requests.post(
-            f"{self.api_url}{model_name}",
-            headers=headers,
-            data=json.dumps({"inputs": inputs})
-        )
-        if response.status_code == 200:
-            return response.json()
-        else:
-            raise Exception(f"Error: {response.status_code} - {response.text}")
-
-    def modify_texture(self, texture_type):
-        return self._request_model("texture-modifier", {"texture": texture_type})
-
-    def adjust_lighting(self, intensity):
-        return self._request_model("lighting-adjuster", {"intensity": intensity})
-
-    def change_perspective(self, perspective):
-        return self._request_model("perspective-changer", {"view": perspective})
-
-    def alter_face(self, features):
-        return self._request_model("face-alteration-model", {"features": features})
-
-    def generate_face(self, characteristics):
-        return self._request_model("face-generator", {"characteristics": characteristics})
-
-    def convert_text_to_3d_mesh(self, text):
-        return self._request_model("text-to-3d-mesh", {"text": text})
-
-    def model_3d_object(self, shape):
-        return self._request_model("3d-object-modeler", {"shape": shape})
-
-@app.route('/modify_texture', methods=['POST'])
-def modify_texture():
-    data = request.json
-    texture_type = data.get('texture_type')
-    result = modeler.modify_texture(texture_type)
-    return jsonify(result)
-
-@app.route('/adjust_lighting', methods=['POST'])
-def adjust_lighting():
-    data = request.json
-    intensity = data.get('intensity')
-    result = modeler.adjust_lighting(intensity)
-    return jsonify(result)
-
-@app.route('/change_perspective', methods=['POST'])
-def change_perspective():
-    data = request.json
-    perspective = data.get('perspective')
-    result = modeler.change_perspective(perspective)
-    return jsonify(result)
-
-@app.route('/alter_face', methods=['POST'])
-def alter_face():
-    data = request.json
-    features = data.get('features')
-    result = modeler.alter_face(features)
-    return jsonify(result)
-
-@app.route('/generate_face', methods=['POST'])
-def generate_face():
-    data = request.json
-    characteristics = data.get('characteristics')
-    result = modeler.generate_face(characteristics)
-    return jsonify(result)
-
-@app.route('/convert_text_to_3d_mesh', methods=['POST'])
-def convert_text_to_3d_mesh():
-    data = request.json
-    text = data.get('text')
-    result = modeler.convert_text_to_3d_mesh(text)
-    return jsonify(result)
-
-@app.route('/model_3d_object', methods=['POST'])
-def model_3d_object():
-    data = request.json
-    shape = data.get('shape')
-    result = modeler.model_3d_object(shape)
-    return jsonify(result)
-
-if __name__ == '__main__':
-    api_token = "YOUR_HUGGING_FACE_API_TOKEN"
-    modeler = HuggingFace3DModels(api_token)
-    app.run(host='0.0.0.0', port=5001/5002)
+import datetime
+import os
+import csv
+import time
+import hashlib
+import logging
+import gradio as gr
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.chrome.options import Options
+from webdriver_manager.chrome import ChromeDriverManager
+from huggingface_hub import InferenceClient
+import random
+import yaml
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Define constants
+DATE_TIME_STR = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+PURPOSE = f"You go to Culvers sites, you continuously seek changes on them since your last observation. Anything new that gets logged and dumped into csv, stored in your log folder at user/app/scraped_data."
+HISTORY = []
+CURRENT_TASK = None
+DEFAULT_FILE_PATH = "user/app/scraped_data/culver/culvers_changes.csv"
+
+# Ensure the directory exists
+os.makedirs(os.path.dirname(DEFAULT_FILE_PATH), exist_ok=True)
+
+# Function to monitor URLs for changes
+def monitor_urls(storage_location, urls, scrape_interval, content_type):
+    global HISTORY
+    previous_hashes = [""] * len(urls)
+
+    try:
+        with webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=Options()) as driver:
+            while True:
+                for i, url in enumerate(urls):
+                    try:
+                        driver.get(url)
+                        time.sleep(2)  # Wait for the page to load
+                        if content_type == "text":
+                            current_content = driver.page_source
+                        elif content_type == "media":
+                            current_content = driver.find_elements_by_tag_name("img")
+                        else:
+                            current_content = driver.page_source
+                        current_hash = hashlib.md5(str(current_content).encode('utf-8')).hexdigest()
+                        if current_hash != previous_hashes[i]:
+                            previous_hashes[i] = current_hash
+                            date_time_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+                            HISTORY.append(f"Change detected at {url} on {date_time_str}")
+                            with open(storage_location, "a", newline="") as csvfile:
+                                csv_writer = csv.DictWriter(csvfile, fieldnames=["date", "time", "url", "change"])
+                                csv_writer.writerow({"date": date_time_str.split()[0], "time": date_time_str.split()[1], "url": url, "change": "Content changed"})
+                                logging.info(f"Change detected at {url} on {date_time_str}")
+                    except Exception as e:
+                        logging.error(f"Error accessing {url}: {e}")
+                time.sleep(scrape_interval * 60)  # Check every scrape_interval minutes
+    except Exception as e:
+        logging.error(f"Error starting ChromeDriver: {e}")
+
+# Define main function to handle user input
+def handle_input(storage_location, urls, scrape_interval, content_type):
+    global CURRENT_TASK, HISTORY
+
+    CURRENT_TASK = f"Monitoring URLs: {', '.join(urls)}"
+    HISTORY.append(f"Task started: {CURRENT_TASK}")
+    monitor_urls(storage_location, urls, scrape_interval, content_type)
+    return TASK_PROMPT.format(task=CURRENT_TASK, history="\n".join(map(str, HISTORY)))
+
+# Load custom prompts
+try:
+    with open("custom_prompts.yaml", "r") as fp:
+        custom_prompts = yaml.safe_load(fp)
+except FileNotFoundError:
+    custom_prompts = {"WEB_DEV": "", "AI_SYSTEM_PROMPT": "", "PYTHON_CODE_DEV": "", "CODE_GENERATION": "", "CODE_INTERPRETATION": "", "CODE_TRANSLATION": "", "CODE_IMPLEMENTATION": ""}
+
+# Define agents
+AGENTS = ["WEB_DEV", "AI_SYSTEM_PROMPT", "PYTHON_CODE_DEV", "CODE_GENERATION", "CODE_INTERPRETATION", "CODE_TRANSLATION", "CODE_IMPLEMENTATION"]
+
+# Define the Mistral inference client
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+# Define the chat response function
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    return generate(message, history, system_message, max_tokens, temperature, top_p)
+
+def start_scraping(storage_location, url1, url2, url3, url4, url5, url6, url7, url8, url9, url10, scrape_interval, content_type):
+    urls = [url for url in [url1, url2, url3, url4, url5, url6, url7, url8, url9, url10] if url]
+    handle_input(storage_location, urls, scrape_interval, content_type)
+    # Start transaction
+    inspector.start_transaction('start_scraping')
+    # Scrape data
+    while True:
+        # Check for scrape_interval
+        time.sleep(scrape_interval * 60)  # Check every scrape_interval minutes
+    # End transaction
+    inspector.end_transaction()
+    return f"Started scraping {', '.join(urls)} every {scrape_interval} minutes."
+
+# Function to display CSV content
+def display_csv(storage_location):
+    if os.path.exists(storage_location):
+        with open(storage_location, "r") as file:
+            return file.read()
+    else:
+        return "No data available."
+
+# Create Gradio interface
+def chat_interface(message, system_message, max_tokens, temperature, top_p, storage_location, url1, url2, url3, url4, url5, url6, url7, url8, url9, url10, scrape_interval, content_type):
+    global HISTORY
+    response = respond(message, HISTORY, system_message, max_tokens, temperature, top_p)
+    HISTORY.append((message, response))
+    return HISTORY, ""
+
+demo = gr.Blocks()
+
+with demo:
+    with gr.Row():
+        with gr.Column():
+            message = gr.Textbox(label="Message")
+            system_message = gr.Textbox(value="You are a friendly Chatbot.", label="System message")
+            max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+            temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+            storage_location = gr.Textbox(value=DEFAULT_FILE_PATH, label="Storage Location")
+            url1 = gr.Textbox(value="https://www.culver.k12.in/", label="URL 1")
+            url2 = gr.Textbox(value="https://www.facebook.com/CulverCommunitySchools", label="URL 2")
+            url3 = gr.Textbox(label="URL 3")
+            url4 = gr.Textbox(label="URL 4")
+            url5 = gr.Textbox(label="URL 5")
+            url6 = gr.Textbox(label="URL 6")
+            url7 = gr.Textbox(label="URL 7")
+            url8 = gr.Textbox(label="URL 8")
+            url9 = gr.Textbox(label="URL 9")
+            url10 = gr.Textbox(label="URL 10")
+            scrape_interval = gr.Slider(minimum=1, maximum=60, value=5, step=1, label="Scrape Interval (minutes)")
+            content_type = gr.Radio(choices=["text", "media", "both"], value="text", label="Content Type")
+            start_button = gr.Button("Start Scraping")
+            csv_output = gr.Textbox(label="CSV Output", interactive=False)
+        
+        with gr.Column():
+            chat_history = gr.Chatbot(label="Chat History")
+            response_box = gr.Textbox(label="Response")
+    
+    start_button.click(start_scraping, inputs=[storage_location, url1, url2, url3, url4, url5, url6, url7, url8, url9, url10, scrape_interval, content_type], outputs=csv_output)
+    message.submit(chat_interface, inputs=[message, system_message, max_tokens, temperature, top_p, storage_location, url1, url2, url3, url4, url5, url6, url7, url8, url9, url10, scrape_interval, content_type], outputs=[chat_history, response_box])
+
+if __name__ == "__main__":
+    demo.launch()