Update app.py

app.py

@@ -1,7 +1,15 @@
 import os
+import sys
 import subprocess
 import streamlit as st
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoConfig, AutoModel
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import black
+from pylint import lint
+from io import StringIO
+import openai
+
+# Set your OpenAI API key here
+openai.api_key = "YOUR_OPENAI_API_KEY"
 
 HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
 PROJECT_ROOT = "projects"
@@ -44,33 +52,24 @@ I am confident that I can leverage my expertise to assist you in developing and
         summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
         summary += "\n\nWorkspace Projects:\n" + "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()])
 
-        # Analyze chat history and workspace projects to suggest actions
-        # Example:
-        # - Check if the user has requested to create a new file
-        # - Check if the user has requested to install a package
-        # - Check if the user has requested to run a command
-        # - Check if the user has requested to generate code
-        # - Check if the user has requested to translate code
-        # - Check if the user has requested to summarize text
-        # - Check if the user has requested to analyze sentiment
-
-        # Generate a response based on the analysis
         next_step = "Based on the current state, the next logical step is to implement the main application logic."
 
         return summary, next_step
 
-def load_hf_token():
-    return 'YOUR_HF_TOKEN'
-
 def save_agent_to_file(agent):
-    """Saves the agent's prompt to a file."""
+    """Saves the agent's prompt to a file locally and then commits to the Hugging Face repository."""
     if not os.path.exists(AGENT_DIRECTORY):
         os.makedirs(AGENT_DIRECTORY)
     file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
+    config_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}Config.txt")
     with open(file_path, "w") as file:
         file.write(agent.create_agent_prompt())
+    with open(config_path, "w") as file:
+        file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}")
     st.session_state.available_agents.append(agent.name)
 
+    commit_and_push_changes(f"Add agent {agent.name}")
+
 def load_agent_prompt(agent_name):
     """Loads an agent prompt from a file."""
     file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
@@ -87,116 +86,162 @@ def create_agent_from_text(name, text):
     save_agent_to_file(agent)
     return agent.create_agent_prompt()
 
+# Chat interface using a selected agent
 def chat_interface_with_agent(input_text, agent_name):
     agent_prompt = load_agent_prompt(agent_name)
     if agent_prompt is None:
         return f"Agent {agent_name} not found."
 
-    model_name = "Bin12345/AutoCoder_S_6.7B"
+    # Load the GPT-2 model which is compatible with AutoModelForCausalLM
+    model_name = "gpt2"
     try:
-        model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=load_hf_token())
-        tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=load_hf_token())
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
         generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
     except EnvironmentError as e:
         return f"Error loading model: {e}"
 
+    # Combine the agent prompt with user input
     combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
     
-    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
+    # Truncate input text to avoid exceeding the model's maximum length
     max_input_length = 900
+    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
     if input_ids.shape[1] > max_input_length:
         input_ids = input_ids[:, :max_input_length]
 
+    # Generate chatbot response
     outputs = model.generate(
-        input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True,
-        pad_token_id=tokenizer.eos_token_id  # Set pad_token_id to eos_token_id
+        input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True, pad_token_id=tokenizer.eos_token_id # Set pad_token_id to eos_token_id
     )
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return response
 
-# Terminal interface
+def workspace_interface(project_name):
+    project_path = os.path.join(PROJECT_ROOT, project_name)
+    if not os.path.exists(PROJECT_ROOT):
+        os.makedirs(PROJECT_ROOT)
+    if not os.path.exists(project_path):
+        os.makedirs(project_path)
+        st.session_state.workspace_projects[project_name] = {"files": []}
+        st.session_state.current_state['workspace_chat']['project_name'] = project_name
+        commit_and_push_changes(f"Create project {project_name}")
+        return f"Project {project_name} created successfully."
+    else:
+        return f"Project {project_name} already exists."
+
+def add_code_to_workspace(project_name, code, file_name):
+    project_path = os.path.join(PROJECT_ROOT, project_name)
+    if os.path.exists(project_path):
+        file_path = os.path.join(project_path, file_name)
+        with open(file_path, "w") as file:
+            file.write(code)
+        st.session_state.workspace_projects[project_name]["files"].append(file_name)
+        st.session_state.current_state['workspace_chat']['added_code'] = {"file_name": file_name, "code": code}
+        commit_and_push_changes(f"Add code to {file_name} in project {project_name}")
+        return f"Code added to {file_name} in project {project_name} successfully."
+    else:
+        return f"Project {project_name} does not exist."
+
 def terminal_interface(command, project_name=None):
     if project_name:
         project_path = os.path.join(PROJECT_ROOT, project_name)
         if not os.path.exists(project_path):
             return f"Project {project_name} does not exist."
-        result = subprocess.run(command, shell=True, capture_output=True, text=True, cwd=project_path)
+        result = subprocess.run(command, cwd=project_path, shell=True, capture_output=True, text=True)
     else:
         result = subprocess.run(command, shell=True, capture_output=True, text=True)
-    return result.stdout
+    if result.returncode == 0:
+        st.session_state.current_state['toolbox']['terminal_output'] = result.stdout
+        return result.stdout
+    else:
+        st.session_state.current_state['toolbox']['terminal_output'] = result.stderr
+        return result.stderr
 
-# Code editor interface
 def code_editor_interface(code):
     try:
         formatted_code = black.format_str(code, mode=black.FileMode())
     except black.NothingChanged:
         formatted_code = code
-
     result = StringIO()
     sys.stdout = result
     sys.stderr = result
-
     (pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True)
     sys.stdout = sys.__stdout__
     sys.stderr = sys.__stderr__
-
     lint_message = pylint_stdout.getvalue() + pylint_stderr.getvalue()
-
+    st.session_state.current_state['toolbox']['formatted_code'] = formatted_code
+    st.session_state.current_state['toolbox']['lint_message'] = lint_message
     return formatted_code, lint_message
 
-# Text summarization tool
 def summarize_text(text):
-    summarizer = pipeline("summarization", model="t5-base", use_auth_token=load_hf_token())
-    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+    summarizer = pipeline("summarization")
+    summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
+    st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text']
     return summary[0]['summary_text']
 
-# Sentiment analysis tool
 def sentiment_analysis(text):
-    analyzer = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment", use_auth_token=load_hf_token())
-    result = analyzer(text)
-    return result[0]['label']
-
-# Text translation tool (code translation)
-def translate_code(code, source_language, target_language):
-    # Use a Hugging Face translation model instead of OpenAI
-    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es", use_auth_token=load_hf_token())  # Example: English to Spanish
-    translated_code = translator(code, target_lang=target_language)[0]['translation_text']
+    analyzer = pipeline("sentiment-analysis")
+    sentiment = analyzer(text)
+    st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
+    return sentiment[0]
+
+def translate_code(code, input_language, output_language):
+    # Define a dictionary to map programming languages to their corresponding file extensions
+    language_extensions = {
+        # ignore the specific languages right now, and continue to EOF
+    }
+
+    # Add code to handle edge cases such as invalid input and unsupported programming languages
+    if input_language not in language_extensions:
+        raise ValueError(f"Invalid input language: {input_language}")
+    if output_language not in language_extensions:
+        raise ValueError(f"Invalid output language: {output_language}")
+
+    # Use the dictionary to map the input and output languages to their corresponding file extensions
+    input_extension = language_extensions[input_language]
+    output_extension = language_extensions[output_language]
+
+    # Translate the code using the OpenAI API
+    prompt = f"Translate this code from {input_language} to {output_language}:\n\n{code}"
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": "You are an expert software developer."},
+            {"role": "user", "content": prompt}
+        ]
+    )
+    translated_code = response.choices[0].message['content'].strip()
+
+    # Return the translated code
+    translated_code = response.choices[0].message['content'].strip()
+    st.session_state.current_state['toolbox']['translated_code'] = translated_code
     return translated_code
 
 def generate_code(code_idea):
-    # Use a Hugging Face code generation model instead of OpenAI
-    generator = pipeline('text-generation', model='bigcode/starcoder', use_auth_token=load_hf_token())
-    generated_code = generator(code_idea, max_length=1000, num_return_sequences=1)[0]['generated_text']
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": "You are an expert software developer."},
+            {"role": "user", "content": f"Generate a Python code snippet for the following idea:\n\n{code_idea}"}
+        ]
+    )
+    generated_code = response.choices[0].message['content'].strip()
+    st.session_state.current_state['toolbox']['generated_code'] = generated_code
     return generated_code
 
-def chat_interface(input_text):
-    """Handles general chat interactions with the user."""
-    # Use a Hugging Face chatbot model or your own logic
-    chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium", use_auth_token=load_hf_token())
-    response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
-    return response
-
-# Workspace interface
-def workspace_interface(project_name):
-    project_path = os.path.join(PROJECT_ROOT, project_name)
-    if not os.path.exists(project_path):
-        os.makedirs(project_path)
-        st.session_state.workspace_projects[project_name] = {'files': []}
-        return f"Project '{project_name}' created successfully."
-    else:
-        return f"Project '{project_name}' already exists."
-
-# Add code to workspace
-def add_code_to_workspace(project_name, code, file_name):
-    project_path = os.path.join(PROJECT_ROOT, project_name)
-    if not os.path.exists(project_path):
-        return f"Project '{project_name}' does not exist."
-    
-    file_path = os.path.join(project_path, file_name)
-    with open(file_path, "w") as file:
-        file.write(code)
-    st.session_state.workspace_projects[project_name]['files'].append(file_name)
-    return f"Code added to '{file_name}' in project '{project_name}'."
+def commit_and_push_changes(commit_message):
+    """Commits and pushes changes to the Hugging Face repository."""
+    commands = [
+        "git add .",
+        f"git commit -m '{commit_message}'",
+        "git push"
+    ]
+    for command in commands:
+        result = subprocess.run(command, shell=True, capture_output=True, text=True)
+        if result.returncode != 0:
+            st.error(f"Error executing command '{command}': {result.stderr}")
+            break
 
 # Streamlit App
 st.title("AI Agent Creator")
@@ -225,7 +270,12 @@ elif app_mode == "Tool Box":
     st.subheader("Chat with CodeCraft")
     chat_input = st.text_area("Enter your message:")
     if st.button("Send"):
-        chat_response = chat_interface(chat_input)
+        if chat_input.startswith("@"):
+            agent_name = chat_input.split(" ")[0][1:]  # Extract agent_name from @agent_name
+            chat_input = " ".join(chat_input.split(" ")[1:])  # Remove agent_name from input
+            chat_response = chat_interface_with_agent(chat_input, agent_name)
+        else:
+            chat_response = chat_interface(chat_input)
         st.session_state.chat_history.append((chat_input, chat_response))
         st.write(f"CodeCraft: {chat_response}")
 
@@ -262,8 +312,8 @@ elif app_mode == "Tool Box":
     # Text Translation Tool (Code Translation)
     st.subheader("Translate Code")
     code_to_translate = st.text_area("Enter code to translate:")
-    source_language = st.text_input("Enter source language (e.g., 'Python'):")
-    target_language = st.text_input("Enter target language (e.g., 'JavaScript'):")
+    source_language = st.text_input("Enter source language (e.g. 'Python'):")
+    target_language = st.text_input("Enter target language (e.g. 'JavaScript'):")
     if st.button("Translate Code"):
         translated_code = translate_code(code_to_translate, source_language, target_language)
         st.code(translated_code, language=target_language.lower())
@@ -275,6 +325,20 @@ elif app_mode == "Tool Box":
         generated_code = generate_code(code_idea)
         st.code(generated_code, language="python")
 
+    # Display Preset Commands
+    st.subheader("Preset Commands")
+    preset_commands = {
+        "Create a new project": "create_project('project_name')",
+        "Add code to workspace": "add_code_to_workspace('project_name', 'code', 'file_name')",
+        "Run terminal command": "terminal_interface('command', 'project_name')",
+        "Generate code": "generate_code('code_idea')",
+        "Summarize text": "summarize_text('text')",
+        "Analyze sentiment": "sentiment_analysis('text')",
+        "Translate code": "translate_code('code', 'source_language', 'target_language')",
+    }
+    for command_name, command in preset_commands.items():
+        st.write(f"{command_name}: `{command}`")
+
 elif app_mode == "Workspace Chat App":
     # Workspace Chat App
     st.header("Workspace Chat App")
@@ -289,7 +353,7 @@ elif app_mode == "Workspace Chat App":
     # Add Code to Workspace
     st.subheader("Add Code to Workspace")
     code_to_add = st.text_area("Enter code to add to workspace:")
-    file_name = st.text_input("Enter file name (e.g., 'app.py'):")
+    file_name = st.text_input("Enter file name (e.g. 'app.py'):")
     if st.button("Add Code"):
         add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
         st.success(add_code_status)
@@ -345,4 +409,8 @@ elif app_mode == "Workspace Chat App":
         st.write("Autonomous Build Summary:")
         st.write(summary)
         st.write("Next Step:")
-        st.write(next_step)
+        st.write(next_step)
+
+# Display current state for debugging
+st.sidebar.subheader("Current State")
+st.sidebar.json(st.session_state.current_state)