Update app.py

app.py

@@ -5,12 +5,9 @@ from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 import black
 from pylint import lint
 from io import StringIO
-import openai
+import together
 import sys
 
-# 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"
 AGENT_DIRECTORY = "agents"
@@ -38,20 +35,25 @@ class AIAgent:
 
     def create_agent_prompt(self):
         skills_str = '\n'.join([f"* {skill}" for skill in self.skills])
-        agent_prompt = f"""
+        agent_prompt = (
+            f"""
 As an elite expert developer, my name is {self.name}. I possess a comprehensive understanding of the following areas:
 {skills_str}
-
 I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications. Please feel free to ask any questions or present any challenges you may encounter.
 """
+        )
         return agent_prompt
 
     def autonomous_build(self, chat_history, workspace_projects):
         """
         Autonomous build logic that continues based on the state of chat history and workspace projects.
         """
-        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()])
+        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:
@@ -64,7 +66,9 @@ I am confident that I can leverage my expertise to assist you in developing and
         # - 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."
+        next_step = (
+            "Based on the current state, the next logical step is to implement the main application logic."
+        )
 
         return summary, next_step
 
@@ -106,8 +110,10 @@ def chat_interface_with_agent(input_text, agent_name):
     except EnvironmentError as e:
         return f"Error loading model: {e}"
 
-    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
-    
+    combined_input = (
+        f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
+    )
+
     input_ids = tokenizer.encode(combined_input, return_tensors="pt")
     max_input_length = 900
     if input_ids.shape[1] > max_input_length:
@@ -152,32 +158,28 @@ def code_editor_interface(code):
 
 # Text summarization tool
 def summarize_text(text):
-    summarizer = pipeline("summarization")
+    summarizer = pipeline("summarization", model="t5-base")
     summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
     return summary[0]['summary_text']
 
 # Sentiment analysis tool
 def sentiment_analysis(text):
-    analyzer = pipeline("sentiment-analysis")
+    analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
     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")  # Example: English to Spanish
     translated_code = translator(code, target_lang=target_language)[0]['translation_text']
     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')
     generated_code = generator(code_idea, max_length=1000, num_return_sequences=1)[0]['generated_text']
     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")
     response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
     return response
@@ -197,7 +199,7 @@ 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)
@@ -233,7 +235,7 @@ elif app_mode == "Tool Box":
     if st.button("Send"):
         chat_response = chat_interface(chat_input)
         st.session_state.chat_history.append((chat_input, chat_response))
-        st.write(f"CodeCraft: {chat_response}")
+        st.write((f"CodeCraft: {chat_response}"))
 
     # Terminal Interface
     st.subheader("Terminal")
@@ -256,14 +258,14 @@ elif app_mode == "Tool Box":
     text_to_summarize = st.text_area("Enter text to summarize:")
     if st.button("Summarize"):
         summary = summarize_text(text_to_summarize)
-        st.write(f"Summary: {summary}")
+        st.write((f"Summary: {summary}"))
 
     # Sentiment Analysis Tool
     st.subheader("Sentiment Analysis")
     sentiment_text = st.text_area("Enter text for sentiment analysis:")
     if st.button("Analyze Sentiment"):
         sentiment = sentiment_analysis(sentiment_text)
-        st.write(f"Sentiment: {sentiment}")
+        st.write((f"Sentiment: {sentiment}"))
 
     # Text Translation Tool (Code Translation)
     st.subheader("Translate Code")
@@ -313,26 +315,26 @@ elif app_mode == "Workspace Chat App":
     if st.button("Get Guidance"):
         chat_response = chat_interface(chat_input)
         st.session_state.chat_history.append((chat_input, chat_response))
-        st.write(f"CodeCraft: {chat_response}")
+        st.write((f"CodeCraft: {chat_response}"))
 
     # Display Chat History
     st.subheader("Chat History")
     for user_input, response in st.session_state.chat_history:
-        st.write(f"User: {user_input}")
-        st.write(f"CodeCraft: {response}")
+        st.write((f"User: {user_input}"))
+        st.write((f"CodeCraft: {response}"))
 
     # Display Terminal History
     st.subheader("Terminal History")
     for command, output in st.session_state.terminal_history:
-        st.write(f"Command: {command}")
+        st.write((f"Command: {command}"))
         st.code(output, language="bash")
 
     # Display Projects and Files
     st.subheader("Workspace Projects")
     for project, details in st.session_state.workspace_projects.items():
-        st.write(f"Project: {project}")
+        st.write((f"Project: {project}"))
         for file in details['files']:
-            st.write(f"  - {file}")
+            st.write((f"  - {file}"))
 
     # Chat with AI Agents
     st.subheader("Chat with AI Agents")
@@ -341,7 +343,7 @@ elif app_mode == "Workspace Chat App":
     if st.button("Send to Agent"):
         agent_chat_response = chat_interface_with_agent(agent_chat_input, selected_agent)
         st.session_state.chat_history.append((agent_chat_input, agent_chat_response))
-        st.write(f"{selected_agent}: {agent_chat_response}")
+        st.write((f"{selected_agent}: {agent_chat_response}"))
 
     # Automate Build Process
     st.subheader("Automate Build Process")
@@ -351,4 +353,4 @@ 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)