Update app.py

app.py

@@ -2,6 +2,25 @@ import streamlit as st
 from huggingface_hub import InferenceClient
 import os
 import pickle
+from langchain.llms import HuggingFaceHub
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain.tools import Tool
+from langchain.agents import ToolAgent, AgentType
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings  # Use Hugging Face Embeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.chains.conversational_retrieval_qa import ConversationalRetrievalQAChain
+from langchain.chains.summarization import load_summarization_chain
+from langchain.chains.base import Chain
+from langchain.chains.llm import LLMChain
+from langchain.prompts import PromptTemplate
+from typing import List, Dict, Any, Optional
 
 st.title("CODEFUSSION ☄")
 
@@ -13,18 +32,19 @@ class Agent:
         self.tools = tools
         self.knowledge_base = knowledge_base
         self.memory = []
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})  # Use a language model for action selection
+        self.agent = ToolAgent(
+            llm=self.llm,
+            tools=self.tools,
+            agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+            verbose=True
+        )
 
     def act(self, prompt, context):
         self.memory.append((prompt, context))
-        action = self.choose_action(prompt, context)
+        action = self.agent.run(prompt, context)
         return action
 
-    def choose_action(self, prompt, context):
-        # Placeholder for action selection logic
-        # This should be implemented based on the specific agent's capabilities
-        # and the available tools
-        return {"tool": "Code Generation", "arguments": {"language": "python", "code": "print('Hello, World!')"}}
-
     def observe(self, observation):
         # Placeholder for observation processing
         # This should be implemented based on the agent's capabilities and the nature of the observation
@@ -54,117 +74,206 @@ class Tool:
 class CodeGenerationTool(Tool):
     def __init__(self):
         super().__init__("Code Generation", "Generates code snippets in various languages.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.prompt_template = PromptTemplate(
+            input_variables=["language", "code_description"],
+            template="Generate {language} code for: {code_description}"
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use a code generation model
         language = arguments.get("language", "python")
-        code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"""{language}{code}"""}
+        code_description = arguments.get("code_description", "print('Hello, World!')")
+        code = self.chain.run(language=language, code_description=code_description)
+        return {"output": code}
 
 class DataRetrievalTool(Tool):
     def __init__(self):
         super().__init__("Data Retrieval", "Accesses data from APIs, databases, or files.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.prompt_template = PromptTemplate(
+            input_variables=["data_source", "data_query"],
+            template="Retrieve data from {data_source} based on: {data_query}"
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use APIs, databases, or file systems
-        source = arguments.get("source", "https://example.com/data")
-        return {"output": f"Data from {source}"}
+        data_source = arguments.get("data_source", "https://example.com/data")
+        data_query = arguments.get("data_query", "some information")
+        data = self.chain.run(data_source=data_source, data_query=data_query)
+        return {"output": data}
 
 class TextGenerationTool(Tool):
     def __init__(self):
         super().__init__("Text Generation", "Generates human-like text based on a given prompt.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.prompt_template = PromptTemplate(
+            input_variables=["text_prompt"],
+            template="Generate text based on: {text_prompt}"
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use a text generation model
-        prompt = arguments.get("prompt", "Write a short story about a cat.")
-        return {"output": f"Generated text: {prompt}"}
+        text_prompt = arguments.get("text_prompt", "Write a short story about a cat.")
+        text = self.chain.run(text_prompt=text_prompt)
+        return {"output": text}
 
 class CodeExecutionTool(Tool):
     def __init__(self):
         super().__init__("Code Execution", "Runs code snippets in various languages.")
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use a code execution engine
         code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"Code executed: {code}"}
+        try:
+            exec(code)
+            return {"output": f"Code executed: {code}"}
+        except Exception as e:
+            return {"output": f"Error executing code: {e}"}
 
 class CodeDebuggingTool(Tool):
     def __init__(self):
         super().__init__("Code Debugging", "Identifies and resolves errors in code snippets.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.prompt_template = PromptTemplate(
+            input_variables=["code", "error_message"],
+            template="Debug the following code:\n{code}\n\nError message: {error_message}"
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use a code debugger
         code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"Code debugged: {code}"}
+        try:
+            exec(code)
+            return {"output": f"Code debugged: {code}"}
+        except Exception as e:
+            error_message = str(e)
+            debugged_code = self.chain.run(code=code, error_message=error_message)
+            return {"output": f"Debugged code:\n{debugged_code}"}
 
 class CodeSummarizationTool(Tool):
     def __init__(self):
         super().__init__("Code Summarization", "Provides a concise overview of the functionality of a code snippet.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.prompt_template = PromptTemplate(
+            input_variables=["code"],
+            template="Summarize the functionality of the following code:\n{code}"
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use a code summarization model
         code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"Code summary: {code}"}
+        summary = self.chain.run(code=code)
+        return {"output": f"Code summary: {summary}"}
 
 class CodeTranslationTool(Tool):
     def __init__(self):
         super().__init__("Code Translation", "Translates code snippets between different programming languages.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.prompt_template = PromptTemplate(
+            input_variables=["code", "target_language"],
+            template="Translate the following code to {target_language}:\n{code}"
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use a code translation model
         code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"Translated code: {code}"}
+        target_language = arguments.get("target_language", "javascript")
+        translated_code = self.chain.run(code=code, target_language=target_language)
+        return {"output": f"Translated code:\n{translated_code}"}
 
 class CodeOptimizationTool(Tool):
     def __init__(self):
         super().__init__("Code Optimization", "Optimizes code for performance and efficiency.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.prompt_template = PromptTemplate(
+            input_variables=["code"],
+            template="Optimize the following code for performance and efficiency:\n{code}"
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use a code optimization model
         code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"Optimized code: {code}"}
+        optimized_code = self.chain.run(code=code)
+        return {"output": f"Optimized code:\n{optimized_code}"}
 
 class CodeDocumentationTool(Tool):
     def __init__(self):
         super().__init__("Code Documentation", "Generates documentation for code snippets.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.prompt_template = PromptTemplate(
+            input_variables=["code"],
+            template="Generate documentation for the following code:\n{code}"
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use a code documentation generator
         code = arguments.get("code", "print('Hello, World!')")
-        return {"output": f"Code documentation: {code}"}
+        documentation = self.chain.run(code=code)
+        return {"output": f"Code documentation:\n{documentation}"}
 
 class ImageGenerationTool(Tool):
     def __init__(self):
         super().__init__("Image Generation", "Generates images based on text descriptions.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.prompt_template = PromptTemplate(
+            input_variables=["description"],
+            template="Generate an image based on the description: {description}"
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use an image generation model
         description = arguments.get("description", "A cat sitting on a couch")
-        return {"output": f"Generated image based on: {description}"}
+        image_url = self.chain.run(description=description)
+        return {"output": f"Generated image: {image_url}"}
 
 class ImageEditingTool(Tool):
     def __init__(self):
         super().__init__("Image Editing", "Modifying existing images.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.prompt_template = PromptTemplate(
+            input_variables=["image_url", "editing_instructions"],
+            template="Edit the image at {image_url} according to the instructions: {editing_instructions}"
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use an image editing library
-        image_path = arguments.get("image_path", "path/to/image.jpg")
-        return {"output": f"Image edited: {image_path}"}
+        image_url = arguments.get("image_url", "https://example.com/image.jpg")
+        editing_instructions = arguments.get("editing_instructions", "Make the cat smile")
+        edited_image_url = self.chain.run(image_url=image_url, editing_instructions=editing_instructions)
+        return {"output": f"Edited image: {edited_image_url}"}
 
 class ImageAnalysisTool(Tool):
     def __init__(self):
         super().__init__("Image Analysis", "Extracting information from images, such as objects, scenes, and emotions.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.prompt_template = PromptTemplate(
+            input_variables=["image_url"],
+            template="Analyze the image at {image_url} and provide information about objects, scenes, and emotions."
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
 
     def run(self, arguments):
-        # This is a simplified example, real implementation would use an image analysis model
-        image_path = arguments.get("image_path", "path/to/image.jpg")
-        return {"output": f"Image analysis results: {image_path}"}
+        image_url = arguments.get("image_url", "https://example.com/image.jpg")
+        analysis_results = self.chain.run(image_url=image_url)
+        return {"output": f"Image analysis results:\n{analysis_results}"}
+
+class QuestionAnsweringTool(Tool):
+    def __init__(self):
+        super().__init__("Question Answering", "Answers questions based on provided context.")
+        self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0.5})
+        self.qa_chain = load_qa_chain(self.llm)  # Use a question answering chain
+
+    def run(self, arguments):
+        question = arguments.get("question", "What is the capital of France?")
+        context = arguments.get("context", "France is a country in Western Europe. Its capital is Paris.")
+        answer = self.qa_chain.run(question=question, context=context)
+        return {"output": answer}
 
 # --- Agent Pool ---
 agent_pool = {
-    "IdeaIntake": Agent("IdeaIntake", "Idea Intake", [DataRetrievalTool(), CodeGenerationTool(), TextGenerationTool()], knowledge_base=""),
-    "CodeBuilder": Agent("CodeBuilder", "Code Builder", [CodeGenerationTool(), CodeDebuggingTool(), CodeOptimizationTool()], knowledge_base=""),
-    "ImageCreator": Agent("ImageCreator", "Image Creator", [ImageGenerationTool(), ImageEditingTool()], knowledge_base=""),
+    "IdeaIntake": Agent("IdeaIntake", "Idea Intake", [DataRetrievalTool(), CodeGenerationTool(), TextGenerationTool(), QuestionAnsweringTool()], knowledge_base=""),
+    "CodeBuilder": Agent("CodeBuilder", "Code Builder", [CodeGenerationTool(), CodeDebuggingTool(), CodeOptimizationTool(), CodeExecutionTool(), CodeSummarizationTool, CodeTranslationTool, CodeDocumentationTool], knowledge_base=""),
+    "ImageCreator": Agent("ImageCreator", "Image Creator", [ImageGenerationTool(), ImageEditingTool(), ImageAnalysisTool], knowledge_base=""),
 }
 
 # --- Workflow Definitions ---
@@ -176,19 +285,13 @@ class Workflow:
         self.description = description
 
     def run(self, prompt, context):
-        # Placeholder for workflow execution logic
-        # This should be implemented based on the specific workflow's steps
-        # and the interaction between the agents
         for agent in self.agents:
             action = agent.act(prompt, context)
-            # Execute the tool
             if action.get("tool"):
                 tool = next((t for t in agent.tools if t.name == action["tool"]), None)
                 if tool:
                     output = tool.run(action["arguments"])
-                    # Update context
                     context.update(output)
-                    # Observe the output
                     agent.observe(output)
         return context
 
@@ -441,7 +544,7 @@ st.write(f"""  Description: {DevSandboxWorkflow().description}""")
 
 # --- Displaying Tool Definitions ---
 st.subheader("Tool Definitions")
-for tool_class in [CodeGenerationTool, DataRetrievalTool, CodeExecutionTool, CodeDebuggingTool, CodeSummarizationTool, CodeTranslationTool, CodeOptimizationTool, CodeDocumentationTool, ImageGenerationTool, ImageEditingTool, ImageAnalysisTool, TextGenerationTool]:
+for tool_class in [CodeGenerationTool, DataRetrievalTool, CodeExecutionTool, CodeDebuggingTool, CodeSummarizationTool, CodeTranslationTool, CodeOptimizationTool, CodeDocumentationTool, ImageGenerationTool, ImageEditingTool, ImageAnalysisTool, TextGenerationTool, QuestionAnsweringTool]:
     tool = tool_class()
     st.write(f"**{tool.name}**")
     st.write(f"  Description: {tool.description}")
@@ -449,10 +552,10 @@ for tool_class in [CodeGenerationTool, DataRetrievalTool, CodeExecutionTool, Cod
 # --- Displaying Example Output ---
 st.subheader("Example Output")
 code_generation_tool = CodeGenerationTool()
-st.write(f"""Code Generation Tool Output: {code_generation_tool.run({'language': 'python', 'code': "print('Hello, World!')"})}""")
+st.write(f"""Code Generation Tool Output: {code_generation_tool.run({'language': 'python', 'code_description': "print('Hello, World!')"})}""")
 
 data_retrieval_tool = DataRetrievalTool()
-st.write(f"""Data Retrieval Tool Output: {data_retrieval_tool.run({'source': 'https://example.com/data'})}""")
+st.write(f"""Data Retrieval Tool Output: {data_retrieval_tool.run({'data_source': 'https://example.com/data', 'data_query': 'some information'})}""")
 
 code_execution_tool = CodeExecutionTool()
 st.write(f"""Code Execution Tool Output: {code_execution_tool.run({'code': "print('Hello, World!')"})}""")
@@ -464,7 +567,7 @@ code_summarization_tool = CodeSummarizationTool()
 st.write(f"""Code Summarization Tool Output: {code_summarization_tool.run({'code': "print('Hello, World!')"})}""")
 
 code_translation_tool = CodeTranslationTool()
-st.write(f"""Code Translation Tool Output: {code_translation_tool.run({'code': "print('Hello, World!')"})}""")
+st.write(f"""Code Translation Tool Output: {code_translation_tool.run({'code': "print('Hello, World!')", 'target_language': 'javascript'})}""")
 
 code_optimization_tool = CodeOptimizationTool()
 st.write(f"""Code Optimization Tool Output: {code_optimization_tool.run({'code': "print('Hello, World!')"})}""")
@@ -476,7 +579,10 @@ image_generation_tool = ImageGenerationTool()
 st.write(f"""Image Generation Tool Output: {image_generation_tool.run({'description': 'A cat sitting on a couch'})}""")
 
 image_editing_tool = ImageEditingTool()
-st.write(f"""Image Editing Tool Output: {image_editing_tool.run({'image_path': 'path/to/image.jpg'})}""")
+st.write(f"""Image Editing Tool Output: {image_editing_tool.run({'image_url': 'https://example.com/image.jpg', 'editing_instructions': 'Make the cat smile'})}""")
 
 image_analysis_tool = ImageAnalysisTool()
-st.write(f"""Image Analysis Tool Output: {image_analysis_tool.run({'image_path': 'path/to/image.jpg'})}""")
+st.write(f"""Image Analysis Tool Output: {image_analysis_tool.run({'image_url': 'https://example.com/image.jpg'})}""")
+
+question_answering_tool = QuestionAnsweringTool()
+st.write(f"""Question Answering Tool Output: {question_answering_tool.run({'question': 'What is the capital of France?', 'context': 'France is a country in Western Europe. Its capital is Paris.'})}""")