Update app.py

app.py

@@ -4,6 +4,8 @@ import random
 import json
 from datetime import datetime
 import gradio as gr  # Corrected import for gradio
+import requests
+from bs4 import BeautifulSoup
 
 class App(gr.Blocks):  # Corrected class inheritance
     def __init__(self):
@@ -19,11 +21,49 @@ class App(gr.Blocks):  # Corrected class inheritance
                 "description": "A clickable button",
                 "code_snippet": "gr.Button(value='{{label}}', variant='primary')"
             },
+            "Textbox": {
+                "properties": {
+                    "label": "Enter text here...",
+                    "lines": 1
+                },
+                "description": "A simple textbox for user input.",
+                "code_snippet": "gr.Textbox(label='{{label}}', lines={{lines}})"
+            },
+            "Slider": {
+                "properties": {
+                    "label": "Adjust the slider:",
+                    "minimum": 0,
+                    "maximum": 100,
+                    "value": 50
+                },
+                "description": "A slider for selecting a value within a range.",
+                "code_snippet": "gr.Slider(label='{{label}}', minimum={{minimum}}, maximum={{maximum}}, value={{value}})"
+            },
+            "Dropdown": {
+                "properties": {
+                    "label": "Select an option:",
+                    "choices": ["Option 1", "Option 2", "Option 3"],
+                    "value": "Option 1"
+                },
+                "description": "A dropdown menu for selecting from a list of options.",
+                "code_snippet": "gr.Dropdown(label='{{label}}', choices={{choices}}, value='{{value}}')"
+            },
+            "Image": {
+                "properties": {
+                    "label": "Upload an image:",
+                    "type": "file"
+                },
+                "description": "An image component for displaying images.",
+                "code_snippet": "gr.Image(label='{{label}}', type='{{type}}')"
+            }
             # ... Other component definitions
         }
         self.nlp_model_names = [
             "google/flan-t5-small",
-            # ... Other NLP model names
+            "facebook/bart-large-cnn",  # Summarization
+            "gpt2",                     # Text Generation
+            "distilbert-base-uncased-finetuned-sst-2-english"  # Sentiment Analysis
+            # ... Other NLP model names from Hugging Face
         ]
         self.nlp_models = []
         # self.initialize_nlp_models()  # Moved to run() for Gradio
@@ -34,7 +74,7 @@ class App(gr.Blocks):  # Corrected class inheritance
             try:
                 # Assuming the use of transformers library for NLP models
                 from transformers import pipeline
-                model = pipeline('text-generation', model=nlp_model_name)
+                model = pipeline('text-generation', model=nlp_model_name)  # Adjust pipeline task if needed
                 self.nlp_models.append(model)
             except Exception as e:
                 print(f"Failed to load model {nlp_model_name}: {e}")
@@ -43,7 +83,7 @@ class App(gr.Blocks):  # Corrected class inheritance
     def get_nlp_response(self, input_text, model_index):
         if self.nlp_models[model_index]:
             response = self.nlp_models[model_index](input_text)
-            return response[0]['generated_text']
+            return response[0]['generated_text']  # Adjust response extraction if needed
         else:
             return "NLP model not available."
 
@@ -122,7 +162,36 @@ class App(gr.Blocks):  # Corrected class inheritance
             return self.get_help_message()
         else:
             return "Unknown command. Type 'get_help_message' for available commands."
+    def execute_code(self, code):
+        try:
+            exec(code)
+        except Exception as e:
+            return f"Error executing code: {e}"
+        return "Code executed successfully"
+    
+    def read_file(self, file_path):
+        try:
+            with open(file_path, 'r') as file:
+                content = file.read()
+            return content
+        except Exception as e:
+            return f"Error reading file: {e}"
 
+    def write_file(self, file_path, content):
+        try:
+            with open(file_path, 'w') as file:
+                file.write(content)
+            return "File written successfully"
+        except Exception as e:
+            return f"Error writing to file: {e}"
+
+    def fetch_web_content(self, url):
+        response = requests.get(url)
+        if response.status_code == 200:
+            soup = BeautifulSoup(response.content, 'html.parser')
+            return soup.prettify()
+        else:
+            return f"Failed to retrieve content. Status code: {response.status_code}"
     def run(self):
         self.initialize_nlp_models()  # Initialize NLP models here
         with gr.Blocks() as demo: