import os import subprocess import random import json from datetime import datetime from huggingface_hub import ( InferenceClient, cached_download, hf_hub_url ) import gradio as gr from safe_search import safe_search from i_search import google from i_search import i_search as i_s from agent import ( ACTION_PROMPT, ADD_PROMPT, COMPRESS_HISTORY_PROMPT, LOG_PROMPT, LOG_RESPONSE, MODIFY_PROMPT, PRE_PREFIX, SEARCH_QUERY, READ_PROMPT, TASK_PROMPT, UNDERSTAND_TEST_RESULTS_PROMPT, ) from utils import ( parse_action, parse_file_content, read_python_module_structure ) from datetime import datetime import json #--- Global Variables for App State --- app_state = {"components": []} terminal_history = "" #--- Component Library --- components_registry = { "Button": { "properties": {"label": "Click Me", "onclick": ""}, "description": "A clickable button", "code_snippet": 'gr.Button(value="{label}", variant="primary")', }, "Text Input": { "properties": {"value": "", "placeholder": "Enter text"}, "description": "A field for entering text", "code_snippet": 'gr.Textbox(label="{placeholder}")', }, "Image": { "properties": {"src": "#", "alt": "Image"}, "description": "Displays an image", "code_snippet": 'gr.Image(label="{alt}")', }, "Dropdown": { "properties": {"choices": ["Option 1", "Option 2"], "value": ""}, "description": "A dropdown menu for selecting options", "code_snippet": 'gr.Dropdown(choices={choices}, label="Dropdown")'}, # Add more components here... } #--- NLP Model (Example using Hugging Face) --- nlp_model_names = [ "google/flan-t5-small", "Qwen/CodeQwen1.5-7B-Chat-GGUF", "bartowski/Codestral-22B-v0.1-GGUF", "bartowski/AutoCoder-GGUF" ] nlp_models = [] for nlp_model_name in nlp_model_names: try: cached_download(hf_hub_url(nlp_model_name, revision="main")) nlp_models.append(InferenceClient(nlp_model_name)) except: nlp_models.append(None) #--- Function to get NLP model response --- def get_nlp_response(input_text, model_index): if nlp_models[model_index]: response = nlp_models[model_index].text_generation(input_text) return response.generated_text else: return "NLP model not available." # --- Component Class --- class Component: def __init__(self, type, properties=None, id=None): self.id = id or random.randint(1000, 9999) self.type = type self.properties = properties or components_registry[type]["properties"].copy() def to_dict(self): return { "id": self.id, "type": self.type, "properties": self.properties, } def render(self): # Properly format choices for Dropdown if self.type == "Dropdown": self.properties["choices"] = ( str(self.properties["choices"]) .replace("[", "") .replace("]", "") .replace("'", "") ) return components_registry[self.type]["code_snippet"].format( **self.properties ) # --- Function to update the app canvas (for preview) --- def update_app_canvas(): components_html = "".join( [ f"<div>Component ID: {component['id']}, Type: {component['type']}, Properties: {component['properties']}</div>" for component in app_state["components"] ] ) return components_html # --- Function to handle component addition --- def add_component(component_type): if component_type in components_registry: new_component = Component(component_type) app_state["components"].append(new_component.to_dict()) return ( update_app_canvas(), f"System: Added component: {component_type}\n", ) else: return None, f"Error: Invalid component type: {component_type}\n" # --- Function to handle terminal input --- def run_terminal_command(command, history): global terminal_history output = "" try: # Basic command parsing (expand with NLP) if command.startswith("add "): component_type = command.split("add ", 1)[1].strip() _, output = add_component(component_type) elif command.startswith("set "): _, output = set_component_property(command) elif command.startswith("search "): search_query = command.split("search ", 1)[1].strip() output = i_s(search_query) elif command.startswith("deploy "): app_name = command.split("deploy ", 1)[1].strip() output = deploy_to_huggingface(app_name) else: # Attempt to execute command as Python code try: result = subprocess.check_output( command, shell=True, stderr=subprocess.STDOUT, text=True ) output = result except Exception as e: output = f"Error executing Python code: {str(e)}" except Exception as e: output = f"Error: {str(e)}" finally: terminal_history += f"User: {command}\n" terminal_history += f"{output}\n" return terminal_history def set_component_property(command): try: # Improved 'set' command parsing set_parts = command.split(" ", 2)[1:] if len(set_parts) != 2: raise ValueError("Invalid 'set' command format.") component_id = int(set_parts[0]) # Use component ID property_name, property_value = set_parts[1].split("=", 1) # Find component by ID component_found = False for component in app_state["components"]: if component["id"] == component_id: if property_name in component["properties"]: component["properties"][ property_name.strip() ] = property_value.strip() component_found = True return ( update_app_canvas(), f"System: Property '{property_name}' set to '{property_value}' for component {component_id}\n", ) else: return ( None, f"Error: Property '{property_name}' not found in component {component_id}\n", ) if not component_found: return ( None, f"Error: Component with ID {component_id} not found.\n", ) except Exception as e: return None, f"Error: Invalid 'set' command format or error setting property: {str(e)}\n" #--- Function to handle chat interaction --- def run_chat(message, history): global terminal_history if message.startswith("!"): command = message[1:] terminal_history = run_terminal_command(command, history) else: # ... (Your regular chat response generation) model_index = 0 # Select the model to use for chat response response = get_nlp_response(message, model_index) if response: return history, terminal_history + f"User: {message}\nAssistant: {response}" else: return history, terminal_history + f"User: {message}\nAssistant: I'm sorry, I couldn't generate a response. Please try again.\n" --- Code Generation --- def generate_python_code(app_name): code = f"""import gradio as gr Define your Gradio components here with gr.Blocks() as {app_name}: """ for component in app_state["components"]: code += " " + Component(**component).render() + "\n" code += f""" {app_name}.launch() """ return code --- Hugging Face Deployment --- def deploy_to_huggingface(app_name): Generate Python code code = generate_python_code(app_name) Create requirements.txt with open("requirements.txt", "w") as f: f.write("gradio==3.32.0\n") Create the app.py file with open("app.py", "w") as f: f.write(code) Execute the deployment command try: subprocess.run( ["huggingface-cli", "repo", "create", "--type", "space", "--space_sdk", "gradio", app_name], check=True ) subprocess.run( ["git", "init"], cwd=f"./{app_name}', check=True ) subprocess.run( ["git", "add", "."], cwd=f'./{app_name}', check=True ) subprocess.run( ['git', 'commit', '-m', '"Initial commit"'], cwd=f'./{app_name}', check=True ) subprocess.run( ["git", "push", "https://huggingface.co/spaces/" + app_name, "main"], cwd=f'./{app_name}', check=True ) return ( f"Successfully deployed to Hugging Face Spaces: https://huggingface.co/spaces/{app\_name}" ) except Exception as e: return f"Error deploying to Hugging Face Spaces: {e}" --- Gradio Interface --- with gr.Blocks() as iface: --- Chat Interface --- chat_history = gr.Chatbot(label="Chat with Agent") chat_input = gr.Textbox(label="Your Message") chat_button = gr.Button("Send") chat_button.click( run_chat, inputs=[chat_input, chat_history], outputs=[chat_history, terminal_output], ) --- Terminal --- terminal_output = gr.Textbox( lines=8, label="Terminal", value=terminal_history ) terminal_input = gr.Textbox(label="Enter Command") terminal_button = gr.Button("Run") terminal_button.click( run_terminal_command, inputs=[terminal_input, terminal_output], outputs=terminal_output, ) iface.launch()

app.py

@@ -46,203 +46,230 @@ terminal_history = ""
 components_registry = { "Button": { "properties": {"label": "Click Me", "onclick": ""}, "description": "A clickable button", "code_snippet": 'gr.Button(value="{label}", variant="primary")', }, "Text Input": { "properties": {"value": "", "placeholder": "Enter text"}, "description": "A field for entering text", "code_snippet": 'gr.Textbox(label="{placeholder}")', }, "Image": { "properties": {"src": "#", "alt": "Image"}, "description": "Displays an image", "code_snippet": 'gr.Image(label="{alt}")', }, "Dropdown": { "properties": {"choices": ["Option 1", "Option 2"], "value": ""}, "description": "A dropdown menu for selecting options", "code_snippet": 'gr.Dropdown(choices={choices}, label="Dropdown")'}, # Add more components here... }
 
 #--- NLP Model (Example using Hugging Face) ---
-nlp_model_name = "google/flan-t5-small"
-
-# Check if the model exists in the cache
-try: cached_download(hf_hub_url(nlp_model_name, revision="main")) nlp_model = InferenceClient(nlp_model_name) except: nlp_model = None
+nlp_model_names = [
+    "google/flan-t5-small",
+    "Qwen/CodeQwen1.5-7B-Chat-GGUF",
+    "bartowski/Codestral-22B-v0.1-GGUF",
+    "bartowski/AutoCoder-GGUF"
+]
+
+nlp_models = []
+
+for nlp_model_name in nlp_model_names:
+    try:
+        cached_download(hf_hub_url(nlp_model_name, revision="main"))
+        nlp_models.append(InferenceClient(nlp_model_name))
+    except:
+        nlp_models.append(None)
 
 #--- Function to get NLP model response ---
-def get_nlp_response(input_text): if nlp_model: response = nlp_model.text_generation(input_text) return response.generated_text else: return "NLP model not available."
+def get_nlp_response(input_text, model_index):
+    if nlp_models[model_index]:
+        response = nlp_models[model_index].text_generation(input_text)
+        return response.generated_text
+    else:
+        return "NLP model not available."
 
 # --- Component Class ---
-class Component: def init(self, type, properties=None, id=None): self.id = id or random.randint(1000, 9999) self.type = type self.properties = properties or components_registry[type]["properties"].copy()
-
-def to_dict(self):
-    return {
-        "id": self.id,
-        "type": self.type,
-        "properties": self.properties,
-    }
-
-def render(self):
-    # Properly format choices for Dropdown
-    if self.type == "Dropdown":
-        self.properties["choices"] = (
-            str(self.properties["choices"])
-            .replace("[", "")
-            .replace("]", "")
-            .replace("'", "")
+class Component:
+    def __init__(self, type, properties=None, id=None):
+        self.id = id or random.randint(1000, 9999)
+        self.type = type
+        self.properties = properties or components_registry[type]["properties"].copy()
+
+    def to_dict(self):
+        return {
+            "id": self.id,
+            "type": self.type,
+            "properties": self.properties,
+        }
+
+    def render(self):
+        # Properly format choices for Dropdown
+        if self.type == "Dropdown":
+            self.properties["choices"] = (
+                str(self.properties["choices"])
+                .replace("[", "")
+                .replace("]", "")
+                .replace("'", "")
+            )
+        return components_registry[self.type]["code_snippet"].format(
+            **self.properties
         )
-    return components_registry[self.type]["code_snippet"].format(
-        **self.properties
-    )
 # --- Function to update the app canvas (for preview) ---
-def update_app_canvas(): components_html = "".join( [ f"<div>Component ID: {component['id']}, Type: {component['type']}, Properties: {component['properties']}</div>" for component in app_state["components"] ] ) return components_html
+def update_app_canvas():
+    components_html = "".join( [ f"<div>Component ID: {component['id']}, Type: {component['type']}, Properties: {component['properties']}</div>" for component in app_state["components"] ] )
+    return components_html
 
 # --- Function to handle component addition ---
-def add_component(component_type): if component_type in components_registry: new_component = Component(component_type) app_state["components"].append(new_component.to_dict()) return ( update_app_canvas(), f"System: Added component: {component_type}\n", ) else: return None, f"Error: Invalid component type: {component_type}\n"
+def add_component(component_type):
+    if component_type in components_registry:
+        new_component = Component(component_type)
+        app_state["components"].append(new_component.to_dict())
+        return (
+            update_app_canvas(),
+            f"System: Added component: {component_type}\n",
+        )
+    else:
+        return None, f"Error: Invalid component type: {component_type}\n"
 
 # --- Function to handle terminal input ---
-def run_terminal_command(command, history): global terminal_history output = "" try: # Basic command parsing (expand with NLP) if command.startswith("add "): component_type = command.split("add ", 1)[1].strip() _, output = add_component(component_type) elif command.startswith("set "): _, output = set_component_property(command) elif command.startswith("search "): search_query = command.split("search ", 1)[1].strip() output = i_s(search_query) elif command.startswith("deploy "): app_name = command.split("deploy ", 1)[1].strip() output = deploy_to_huggingface(app_name) else: # Attempt to execute command as Python code try: result = subprocess.check_output( command, shell=True, stderr=subprocess.STDOUT, text=True ) output = result except Exception as e: output = f"Error executing Python code: {str(e)}" except Exception as e: output = f"Error: {str(e)}" finally: terminal_history += f"User: {command}\n" terminal_history += f"{output}\n" return terminal_history
-
-def set_component_property(command): try: # Improved 'set' command parsing set_parts = command.split(" ", 2)[1:] if len(set_parts) != 2: raise ValueError("Invalid 'set' command format.")
-
-component_id = int(set_parts[0])  # Use component ID
-    property_name, property_value = set_parts[1].split("=", 1)
-
-    # Find component by ID
-    component_found = False
-    for component in app_state["components"]:
-        if component["id"] == component_id:
-            if property_name in component["properties"]:
-                component["properties"][
-                    property_name.strip()
-                ] = property_value.strip()
-                component_found = True
-                return (
-                    update_app_canvas(),
-                    f"System: Property '{property_name}' set to '{property_value}' for component {component_id}\n",
+def run_terminal_command(command, history):
+    global terminal_history
+    output = ""
+    try:
+        # Basic command parsing (expand with NLP)
+        if command.startswith("add "):
+            component_type = command.split("add ", 1)[1].strip()
+            _, output = add_component(component_type)
+        elif command.startswith("set "):
+            _, output = set_component_property(command)
+        elif command.startswith("search "):
+            search_query = command.split("search ", 1)[1].strip()
+            output = i_s(search_query)
+        elif command.startswith("deploy "):
+            app_name = command.split("deploy ", 1)[1].strip()
+            output = deploy_to_huggingface(app_name)
+        else:
+            # Attempt to execute command as Python code
+            try:
+                result = subprocess.check_output(
+                    command, shell=True, stderr=subprocess.STDOUT, text=True
                 )
-            else:
-                return (
-                    None,
-                    f"Error: Property '{property_name}' not found in component {component_id}\n",
-                )
-    if not component_found:
-        return (
-            None,
-            f"Error: Component with ID {component_id} not found.\n",
-        )
+                output = result
+            except Exception as e:
+                output = f"Error executing Python code: {str(e)}"
+    except Exception as e:
+        output = f"Error: {str(e)}"
+    finally:
+        terminal_history += f"User: {command}\n"
+        terminal_history += f"{output}\n"
+    return terminal_history
+
+def set_component_property(command):
+    try:
+        # Improved 'set' command parsing
+        set_parts = command.split(" ", 2)[1:]
+        if len(set_parts) != 2:
+            raise ValueError("Invalid 'set' command format.")
+
+        component_id = int(set_parts[0])  # Use component ID
+        property_name, property_value = set_parts[1].split("=", 1)
+
+        # Find component by ID
+        component_found = False
+        for component in app_state["components"]:
+            if component["id"] == component_id:
+                if property_name in component["properties"]:
+                    component["properties"][
+                        property_name.strip()
+                    ] = property_value.strip()
+                    component_found = True
+                    return (
+                        update_app_canvas(),
+                        f"System: Property '{property_name}' set to '{property_value}' for component {component_id}\n",
+                    )
+                else:
+                    return (
+                        None,
+                        f"Error: Property '{property_name}' not found in component {component_id}\n",
+                    )
+        if not component_found:
+            return (
+                None,
+                f"Error: Component with ID {component_id} not found.\n",
+            )
+
+    except Exception as e:
+        return None, f"Error: Invalid 'set' command format or error setting property: {str(e)}\n"
 
-except Exception as e:
-    return None, f"Error: Invalid 'set' command format or error setting property: {str(e)}\n"
 #--- Function to handle chat interaction ---
-def run_chat(message, history): global terminal_history if message.startswith("!"): command = message[1:] terminal_history = run_terminal_command(command, history) return history, terminal_history else: # ... (Your regular chat response generation) return history, terminal_history
-
-# --- Code Generation ---
-def generate_python_code(app_name): code = f""" import gradio as gr
+def run_chat(message, history):
+    global terminal_history
+    if message.startswith("!"):
+        command = message[1:]
+        terminal_history = run_terminal_command(command, history)
+    else:
+        # ... (Your regular chat response generation)
+        model_index = 0  # Select the model to use for chat response
+        response = get_nlp_response(message, model_index)
+if response:
+return history, terminal_history + f"User: {message}\nAssistant: {response}"
+else:
+return history, terminal_history + f"User: {message}\nAssistant: I'm sorry, I couldn't generate a response. Please try again.\n"
+
+--- Code Generation ---
+def generate_python_code(app_name):
+code = f"""import gradio as gr
 Define your Gradio components here
 with gr.Blocks() as {app_name}: """ for component in app_state["components"]: code += " " + Component(**component).render() + "\n"
 
 code += f"""
 {app_name}.launch() """ return code
 
-# --- Save/Load App State ---
-def save_app_state(filename="app_state.json"): with open(filename, "w") as f: json.dump(app_state, f)
+--- Hugging Face Deployment ---
+def deploy_to_huggingface(app_name):
 
-# --- Hugging Face Deployment --- def deploy_to_huggingface(app_name): # Generate Python code code = generate_python_code(app_name)
-def load_app_state(filename="app_state.json"): global app_state try: with open(filename, "r") as f: app_state = json.load(f) except FileNotFoundError: print("App state file not found. Starting with a blank slate.")
+Generate Python code
+code = generate_python_code(app_name)
 
-# Create requirements.txt
+Create requirements.txt
 with open("requirements.txt", "w") as f:
-    f.write("gradio==3.32.0\n")
+f.write("gradio==3.32.0\n")
 
-# Create the app.py file
+Create the app.py file
 with open("app.py", "w") as f:
-    f.write(code)
+f.write(code)
 
-# Execute the deployment command
+Execute the deployment command
 try:
-    subprocess.run(
-        [
-            "huggingface-cli",
-            "repo",
-            "create",
-            "--type",
-            "space",
-            "--space_sdk",
-            "gradio",
-            app_name,
-        ],
-        check=True,
-    )
-    subprocess.run(
-        ["git", "init"], cwd=f"./{app_name}", check=True
-    )
-    subprocess.run(
-        ["git", "add", "."], cwd=f"./{app_name}", check=True
-    )
-    subprocess.run(
-        ['git', 'commit', '-m', '"Initial commit"'], cwd=f"./{user_name}/{app_name}", check=True
-    )
-    subprocess.run(
-        ["git", "push", "https://huggingface.co/spaces/" + app_name, "main"], cwd=f"./{app_name}", check=True
-    )
-    return (
-        f"Successfully deployed to Hugging Face Spaces: https://huggingface.co/spaces/{app_name}"
-    )
+subprocess.run(
+["huggingface-cli", "repo", "create", "--type", "space", "--space_sdk", "gradio", app_name],
+check=True
+)
+subprocess.run(
+["git", "init"], cwd=f"./{app_name}', check=True
+)
+subprocess.run(
+["git", "add", "."], cwd=f'./{app_name}', check=True
+)
+subprocess.run(
+['git', 'commit', '-m', '"Initial commit"'], cwd=f'./{app_name}', check=True
+)
+subprocess.run(
+["git", "push", "https://huggingface.co/spaces/" + app_name, "main"], cwd=f'./{app_name}', check=True
+)
+return (
+f"Successfully deployed to Hugging Face Spaces: https://huggingface.co/spaces/{app\_name}"
+)
 except Exception as e:
-    return f"Error deploying to Hugging Face Spaces: {e}"
+return f"Error deploying to Hugging Face Spaces: {e}"
+
 --- Gradio Interface ---
-with gr.Blocks() as iface: with gr.Row(): # --- Chat Interface --- chat_history = gr.Chatbot(label="Chat with Agent") chat_input = gr.Textbox(label="Your Message") chat_button = gr.Button("Send")
+with gr.Blocks() as iface:
+
+--- Chat Interface ---
+chat_history = gr.Chatbot(label="Chat with Agent")
+chat_input = gr.Textbox(label="Your Message")
+chat_button = gr.Button("Send")
 
 chat_button.click(
-        run_chat,
-        inputs=[chat_input, chat_history],
-        outputs=[chat_history, terminal_output],
-    )
-
-with gr.Row():
-    # --- App Builder Section ---
-    app_canvas = gr.HTML(
-        "<div>App Canvas Preview:</div>", label="App Canvas"
-    )
-    with gr.Column():
-        component_list = gr.Dropdown(
-            choices=list(components_registry.keys()), label="Components"
-        )
-        add_button = gr.Button("Add Component")
+run_chat,
+inputs=[chat_input, chat_history],
+outputs=[chat_history, terminal_output],
+)
 
-        add_button.click(
-            add_component,
-            inputs=component_list,
-            outputs=[app_canvas, terminal_output],
-        )
+--- Terminal ---
+terminal_output = gr.Textbox(
+lines=8, label="Terminal", value=terminal_history
+)
+terminal_input = gr.Textbox(label="Enter Command")
+terminal_button = gr.Button("Run")
+
+terminal_button.click(
+run_terminal_command,
+inputs=[terminal_input, terminal_output],
+outputs=terminal_output,
+)
 
-with gr.Row():
-    # --- Terminal ---
-    terminal_output = gr.Textbox(
-        lines=8, label="Terminal", value=terminal_history
-    )
-    terminal_input = gr.Textbox(label="Enter Command")
-    terminal_button = gr.Button("Run")
-
-    terminal_button.click(
-        run_terminal_command,
-        inputs=[terminal_input, terminal_output],
-        outputs=terminal_output,
-    )
-
-with gr.Row():
-    # --- Code Generation ---
-    code_output = gr.Code(
-        generate_python_code("app_name"),
-        language="python",
-        label="Generated Code",
-    )
-    app_name_input = gr.Textbox(label="App Name")
-    generate_code_button = gr.Button("Generate Code")
-    generate_code_button.click(
-        generate_python_code,
-        inputs=[app_name_input],
-        outputs=code_output,
-    )
-
-with gr.Row():
-    # --- Save/Load Buttons ---
-    save_button = gr.Button("Save App State")
-    load_button = gr.Button("Load App State")
-
-    save_button.click(save_app_state)
-    load_button.click(load_app_state)
-
-with gr.Row():
-    # --- Deploy Button ---
-    deploy_button = gr.Button("Deploy to Hugging Face")
-    deploy_output = gr.Textbox(label="Deployment Output")
-    deploy_button.click(
-        deploy_to_huggingface,
-        inputs=[app_name_input],
-        outputs=[deploy_output],
-    )
 iface.launch()