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  1. src/agents/product_manager_agent.py +14 -0
  2. src/agents/project_manager_agent.py +14 -0
  3. src/agents/quality_assurance_agent.py +14 -0
  4. src/agents/software_architect_agent.py +28 -0
  5. src/agents/software_engineer_agent.py +14 -0
  6. src/utils/run_pipeline_and_save.py +24 -0
  7. src/utils/zip_output.py +12 -0
src/agents/product_manager_agent.py ADDED
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ import torch
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+
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+ MODEL_REPO = "Rahul-8799/product_manager_mistral"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
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+ model = AutoModelForCausalLM.from_pretrained(MODEL_REPO, device_map="auto", torch_dtype=torch.float16)
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+ model.eval()
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+
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+ def run(prompt):
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+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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+ with torch.no_grad():
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+ outputs = model.generate(**inputs, max_new_tokens=512)
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+ return tokenizer.decode(outputs[0], skip_special_tokens=True)
src/agents/project_manager_agent.py ADDED
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ import torch
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+
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+ MODEL_REPO = "Rahul-8799/project_manager_gemma3"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
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+ model = AutoModelForCausalLM.from_pretrained(MODEL_REPO, device_map="auto", torch_dtype=torch.float16)
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+ model.eval()
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+
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+ def run(prompt):
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+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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+ with torch.no_grad():
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+ outputs = model.generate(**inputs, max_new_tokens=512)
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+ return tokenizer.decode(outputs[0], skip_special_tokens=True)
src/agents/quality_assurance_agent.py ADDED
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ import torch
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+
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+ MODEL_REPO = "Rahul-8799/quality_assurance_stablecode"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
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+ model = AutoModelForCausalLM.from_pretrained(MODEL_REPO, device_map="auto", torch_dtype=torch.float16)
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+ model.eval()
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+
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+ def run(prompt):
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+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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+ with torch.no_grad():
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+ outputs = model.generate(**inputs, max_new_tokens=512)
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+ return tokenizer.decode(outputs[0], skip_special_tokens=True)
src/agents/software_architect_agent.py ADDED
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+ from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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+ import torch
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+
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+ MODEL_REPO = "Rahul-8799/software_architect_command_r"
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+
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+ bnb_config = BitsAndBytesConfig(
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+ load_in_4bit=True,
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+ bnb_4bit_compute_dtype=torch.float16,
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+ bnb_4bit_use_double_quant=True,
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+ bnb_4bit_quant_type="nf4"
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+ )
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+
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+ tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
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+
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+ model = AutoModelForCausalLM.from_pretrained(
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+ MODEL_REPO,
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+ quantization_config=bnb_config,
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+ device_map={"": 0}, # force full GPU load
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+ torch_dtype=torch.float16
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+ )
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+
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+ model.eval()
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+
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+ def run(prompt):
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+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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+ with torch.no_grad():
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+ outputs = model.generate(**inputs, max_new_tokens=512)
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+ return tokenizer.decode(outputs[0], skip_special_tokens=True)
src/agents/software_engineer_agent.py ADDED
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ import torch
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+
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+ MODEL_REPO = "Rahul-8799/software_engineer_mellum"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
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+ model = AutoModelForCausalLM.from_pretrained(MODEL_REPO, device_map="auto", torch_dtype=torch.float16)
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+ model.eval()
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+
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+ def run(prompt):
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+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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+ with torch.no_grad():
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+ outputs = model.generate(**inputs, max_new_tokens=512)
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+ return tokenizer.decode(outputs[0], skip_special_tokens=True)
src/utils/run_pipeline_and_save.py ADDED
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+ import os
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+ import json
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+
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+ def run_pipeline_and_save(prompt):
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+ os.makedirs("output", exist_ok=True)
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+
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+ # Simulate agent conversation
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+ conversation = [
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+ {"Product Manager": "Turn the idea into a basic spec."},
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+ {"Project Manager": "Planned a timeline with 3 milestones."},
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+ {"Software Architect": "Created folder structure and basic APIs."},
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+ {"Software Engineer": "<html><body><h1>Hello World</h1></body></html>"},
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+ {"Quality Assurance": "HTML renders correctly. No issues found."}
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+ ]
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+
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+ fake_html = "<html><body><h1>Hello World</h1></body></html>"
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+
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+ with open("output/generated_ui.html", "w") as f:
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+ f.write(fake_html)
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+
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+ with open("output/agent_log.json", "w") as f:
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+ json.dump(conversation, f, indent=2)
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+
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+ return conversation, fake_html
src/utils/zip_output.py ADDED
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+ import zipfile
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+ import os
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+
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+ def zip_output():
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+ zip_path = "output_bundle.zip"
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+ with zipfile.ZipFile(zip_path, "w") as zipf:
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+ for root, dirs, files in os.walk("output"):
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+ for file in files:
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+ file_path = os.path.join(root, file)
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+ arcname = os.path.relpath(file_path, "output")
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+ zipf.write(file_path, arcname=os.path.join("output", arcname))
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+ return zip_path