added more model provider and models and increase context

SDLC.py

@@ -1,4 +1,4 @@
-# newSDLC.py
+# SDLC.py
 import os
 import sys
 import shutil
@@ -10,8 +10,8 @@ from langchain_core.language_models.base import BaseLanguageModel # Correct impo
 from langchain_groq import ChatGroq
 from langchain_openai import ChatOpenAI
 # Add imports for other potential providers if needed
-# from langchain_google_genai import ChatGoogleGenerativeAI
-# from langchain_anthropic import ChatAnthropic
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_anthropic import ChatAnthropic
 from tavily import TavilyClient
 from dotenv import load_dotenv
 import operator
@@ -198,10 +198,18 @@ def initialize_llm_clients(provider: str, model_name: str, llm_api_key: str, tav
         elif provider_lower == "groq":
             llm_instance = ChatGroq(model=model_name, temperature=0.5, api_key=llm_api_key)
         # Add elif blocks for other providers here
-        # elif provider_lower == "google":
-        #     llm_instance = ChatGoogleGenerativeAI(model=model_name, google_api_key=llm_api_key, temperature=0.5)
-        # elif provider_lower == "anthropic":
-        #     llm_instance = ChatAnthropic(model=model_name, anthropic_api_key=llm_api_key, temperature=0.5)
+        elif provider_lower == "google":
+            llm_instance = ChatGoogleGenerativeAI(model=model_name, google_api_key=llm_api_key, temperature=0.5)
+        elif provider_lower == "anthropic":
+            llm_instance = ChatAnthropic(model=model_name, anthropic_api_key=llm_api_key, temperature=0.5)
+        elif provider_lower == "xai":
+            # Adding support for xAI's Grok, assuming an OpenAI-compatible API
+            llm_instance = ChatOpenAI(
+                model=model_name,
+                temperature=0.5,
+                api_key=llm_api_key,
+                base_url="https://api.x.ai"  # Replace with the actual xAI API endpoint
+            )
         else:
             raise ValueError(f"Unsupported LLM provider: {provider}")
 
@@ -677,7 +685,7 @@ def generate_initial_code(state: MainState) -> MainState:
     if not llm: raise ConnectionError("LLM instance not found in state.")
     if 'messages' not in state: state['messages'] = []
     uml_types = ', '.join([c.diagram_type for c in state.get('final_uml_codes', [])])
-    prompt = f"Generate complete, runnable '{state['coding_language']}' project for '{state['project']}'. Base on Design Doc, User Stories, and UML ({uml_types}). Include main scripts, modules, requirements, basic README, comments.\nDesign:\n{state.get('final_design_document', 'N/A')}\nStories (Context):\n{state.get('final_user_story', 'N/A')[:1000]}...\n---\nOutput ONLY JSON (GeneratedCode model)."
+    prompt = f"Generate complete, runnable '{state['coding_language']}' project for '{state['project']}'. Base on Design Doc, User Stories, and UML ({uml_types}). Include main scripts, modules, requirements, basic README, comments.\nDesign:\n{state.get('final_design_document', 'N/A')}\nStories (Context):\n{state.get('final_user_story', 'N/A')}...\n---\nOutput ONLY JSON (GeneratedCode model)."
     structured_llm = llm.with_structured_output(GeneratedCode) # Use LLM from state
     response = structured_llm.invoke(prompt)
     if not response or not isinstance(response, GeneratedCode) or not response.files:
@@ -722,7 +730,7 @@ def generate_code_feedback(state: MainState) -> MainState:
     func_name = "generate_code_feedback"
     code_c = state.get("code_current"); instructions = ""
     # --- CORRECTED LOOP ---
-    code_str_parts = []; total_len = 0; max_code_len = 4000
+    code_str_parts = []; total_len = 0; max_code_len = 250000
     files_to_process = code_c.files if code_c and isinstance(code_c, GeneratedCode) else []
     if not files_to_process: logger.warning(f"No files in code_current for {func_name}"); code_content = "No code files provided."; instructions = "N/A"
     else:
@@ -754,7 +762,7 @@ def refine_code(state: MainState) -> MainState:
     func_name = "refine_code"
     code_c = state.get("code_current"); instructions = ""
     # --- CORRECTED LOOP ---
-    code_str_parts = []; total_len = 0; max_code_len = 3000
+    code_str_parts = []; total_len = 0; max_code_len = 25000
     files_to_process = code_c.files if code_c and isinstance(code_c, GeneratedCode) else []
     if not files_to_process: logger.warning(f"No files in code_current for {func_name}"); code_content = "No previous code."; instructions = state.get("code_current", GeneratedCode(files=[], instructions="")).instructions
     else:
@@ -791,7 +799,7 @@ def code_review(state: MainState) -> MainState:
     code_files_to_review = state.get("final_code_files", [])
     if not code_files_to_review: logger.warning(f"No files in final_code_files for {func_name}"); state["code_review_current_feedback"] = "No code available."; state["messages"].append(AIMessage(content="Code Review: No code.")); return state
     # --- CORRECTED LOOP ---
-    code_str_parts = []; total_len = 0; max_code_len = 4000
+    code_str_parts = []; total_len = 0; max_code_len = 25000
     instructions = state.get("code_current", GeneratedCode(files=[], instructions="")).instructions
     files_to_process = code_files_to_review
     for file in files_to_process:
@@ -822,7 +830,7 @@ def security_check(state: MainState) -> MainState:
     code_files_to_check = state.get("final_code_files", [])
     if not code_files_to_check: logger.warning(f"No files in final_code_files for {func_name}"); state["security_current_feedback"] = "No code available."; state["messages"].append(AIMessage(content="Security Check: No code.")); return state
     # --- CORRECTED LOOP ---
-    code_str_parts = []; total_len = 0; max_code_len = 4000
+    code_str_parts = []; total_len = 0; max_code_len = 25000
     instructions = state.get("code_current", GeneratedCode(files=[], instructions="")).instructions
     files_to_process = code_files_to_check
     for file in files_to_process:
@@ -854,7 +862,7 @@ def refine_code_with_reviews(state: MainState) -> MainState:
     if not code_files_to_refine: logger.error(f"No files in final_code_files for {func_name}"); raise ValueError("No code available.")
     instructions = state.get("code_current", GeneratedCode(files=[], instructions="")).instructions
     # --- CORRECTED LOOP ---
-    code_str_parts = []; total_len = 0; max_code_len = 3000
+    code_str_parts = []; total_len = 0; max_code_len = 25000
     files_to_process = code_files_to_refine
     if not files_to_process: logger.warning(f"No files for {func_name}"); code_content = "No previous code."
     else:
@@ -941,7 +949,7 @@ def generate_initial_test_cases(state: MainState) -> MainState:
     if 'messages' not in state: state['messages'] = []
     func_name = "generate_initial_test_cases"
     # --- RECOMMENDED: Use corrected loop ---
-    code_str_parts = []; total_len = 0; max_code_len = 2000
+    code_str_parts = []; total_len = 0; max_code_len = 25000
     files_to_process = state.get("final_code_files", [])
     if not files_to_process: logger.warning(f"No files for {func_name}"); code_str = "No code files provided."
     else:
@@ -997,7 +1005,7 @@ def refine_test_cases_and_code(state: MainState) -> MainState:
     if not current_tests or not current_code_files: logger.error(f"Missing tests or code for {func_name}"); raise ValueError("Missing data.")
     tests_str = "\n".join([f"- {tc.description}: Input={tc.input_data}, Expected={tc.expected_output}" for tc in current_tests])
     # --- CORRECTED LOOP ---
-    code_str_parts = []; total_len = 0; max_code_len = 3000
+    code_str_parts = []; total_len = 0; max_code_len = 25000
     files_to_process = current_code_files
     if not files_to_process: logger.warning(f"No files for {func_name}"); code_str = "No code."
     else:
@@ -1082,7 +1090,7 @@ def generate_initial_quality_analysis(state: MainState) -> MainState:
     instructions = state.get("code_current", GeneratedCode(files=[], instructions="")).instructions
     if not code_files_passed: logger.warning(f"No tested code for {func_name}."); state["quality_current_analysis"] = "No passed code available."; return state
     # --- CORRECTED LOOP ---
-    code_str_parts = []; total_len = 0; max_code_len = 4000
+    code_str_parts = []; total_len = 0; max_code_len = 25000
     files_to_process = code_files_passed
     if not files_to_process: logger.error(f"Logic error: files_to_process empty in {func_name}"); code_str = "Error retrieving code."
     else:
@@ -1131,7 +1139,7 @@ def refine_quality_and_code(state: MainState) -> MainState:
     code_files_base = state.get("final_test_code_files", [])
     instructions = state.get("code_current", GeneratedCode(files=[], instructions="")).instructions
     # --- CORRECTED LOOP ---
-    code_str_parts = []; total_len = 0; max_code_len = 2000
+    code_str_parts = []; total_len = 0; max_code_len = 25000
     files_to_process = code_files_base
     if not files_to_process: logger.warning(f"No tested code for {func_name}"); code_content = "N/A"
     else:
@@ -1209,7 +1217,7 @@ def generate_initial_deployment(state: MainState, prefs: str) -> MainState:
     if not final_code: logger.error(f"No final code for {func_name}"); raise ValueError("Final code missing.")
     instructions = state.get("code_current", GeneratedCode(files=[], instructions="")).instructions
     # --- CORRECTED LOOP ---
-    code_str_parts = []; total_len = 0; max_code_len = 2000
+    code_str_parts = []; total_len = 0; max_code_len = 25000
     files_to_process = final_code
     if not files_to_process: logger.warning(f"No files for {func_name}"); code_context = "No code files."
     else:
@@ -1258,7 +1266,7 @@ def refine_deployment(state: MainState) -> MainState:
     func_name = "refine_deployment"
     current_plan = state.get('deployment_current_process', 'N/A'); ai_feedback = state.get('deployment_feedback', 'N/A'); human_feedback = state.get('deployment_human_feedback', 'N/A')
     # --- ADDED LOOP ---
-    code_str_parts = []; total_len = 0; max_code_len = 2000
+    code_str_parts = []; total_len = 0; max_code_len = 25000
     final_code = state.get("final_code_files", []); instructions = state.get("code_current", GeneratedCode(files=[], instructions="")).instructions
     files_to_process = final_code
     if not files_to_process: logger.warning(f"No files for {func_name}"); code_context = "No code files."
@@ -1298,4 +1306,4 @@ def save_final_deployment_plan(state: MainState) -> MainState:
     state["final_deployment_path"] = filepath
     return state
 
-# --- END OF newSDLC.py ---
+# --- END OF SDLC.py ---

app.py

@@ -54,10 +54,41 @@ if not logger.handlers:
     logger.info("Streamlit app logger configured.")
 
 # --- Constants for Configuration ---
+# Define available providers and their models
 AVAILABLE_MODELS = {
-    "OpenAI": ["gpt-4o", "gpt-4-turbo", "gpt-3.5-turbo"],
-    "Groq": ["llama3-8b-8192", "llama3-70b-8192", "mixtral-8x7b-32768", "gemma-7b-it"],
-    # Add more providers and models here if supported in initialize_llm_clients
+    "OpenAI": [
+        "gpt-4o-mini", "gpt-4o-mini-2024-07-18",
+        "gpt-4o", "gpt-4o-2024-08-06",
+        "o1-mini", "o1-mini-2024-09-12",
+        "o3-mini", "o3-mini-2025-01-31",
+    ],
+    "Groq": [
+        "llama3-8b-8192", "llama3-70b-8192", "llama-3.1-8b-instant",
+        "llama-3.2-1b-preview", "llama-3.2-3b-preview", "llama-3.3-70b-specdec",
+        "llama-3.3-70b-versatile", "mistral-saba-24b", "gemma2-9b-it",
+        "deepseek-r1-distill-llama-70b", "deepseek-r1-distill-qwen-32b",
+        "qwen-2.5-32b", "qwen-2.5-coder-32b", "qwen-qwq-32b",
+        "mixtral-8x7b-32768",
+    ],
+    "Google": [
+        "gemini-1.5-pro-latest", "gemini-1.5-flash-latest",
+        "gemini-1.0-pro", "gemini-1.0-flash", "gemini-2.5-pro-exp-03-25", "gemini-2.0-flash", 
+    ],
+    "Anthropic": [
+        # Use API Identifiers (usually include date)
+        "claude-3-opus-20240229",
+        "claude-3-sonnet-20240229",
+        "claude-3-haiku-20240307",
+        "claude-3-5-haiku-latest", 
+        "claude-3-5-sonnet-latest", 
+        "claude-3-7-sonnet-latest"
+    ],
+    "xAI": [
+        "grok-1", # Primary model available via API
+         "grok-2-latest",
+        "grok-3", 
+        "grok-3-mini"
+    ]
 }
 LLM_PROVIDERS = list(AVAILABLE_MODELS.keys())
 

requirements.txt

@@ -1,32 +1,33 @@
-# Core Streamlit UI
-streamlit
-
-# LangChain Framework & Integrations
-# NOTE: LangChain releases often, pin versions for stability
-langchain
-langchain-core
-langchain-openai
-langchain-groq
-# Add other langchain provider packages if you implement them (e.g., langchain-google-genai, langchain-anthropic)
-
-# Web Search Client
-tavily-python
-
-# Data Validation
-pydantic
-
-# Retry Logic
-tenacity
-
-# UML Diagram Generation (Requires Java runtime)
-plantuml
-
-# Environment Variable Loading
-python-dotenv
-
-# Typing Helpers (Often a dependency, but good to include explicitly)
-typing-extensions
-
-# HTTP Clients (Often dependencies of LLM/API clients, pin for stability)
-httpx
-requests
+# Core Streamlit UI
+streamlit>=1.30.0,<2.0.0
+
+# LangChain Framework & Integrations
+# NOTE: LangChain releases often, pin versions for stability
+langchain>=0.1.14,<0.2.0
+langchain-core>=0.1.40,<0.2.0
+langchain-openai>=0.1.1,<0.2.0
+langchain-groq>=0.1.2,<0.2.0
+langchain-google-genai
+langchain-anthropic
+
+# Web Search Client
+tavily-python>=0.3.3,<0.4.0
+
+# Data Validation
+pydantic>=2.0.0,<3.0.0
+
+# Retry Logic
+tenacity>=8.2.0,<9.0.0
+
+# UML Diagram Generation (Requires Java runtime)
+plantuml>=0.3.0,<0.4.0
+
+# Environment Variable Loading
+python-dotenv>=1.0.0,<2.0.0
+
+# Typing Helpers (Often a dependency, but good to include explicitly)
+typing-extensions>=4.8.0,<5.0.0
+
+# HTTP Clients (Often dependencies of LLM/API clients, pin for stability)
+httpx>=0.27.0,<0.28.0
+requests>=2.31.0,<3.0.0