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app.py

@@ -1,48 +1,179 @@
+\
+import os
+import re
+import json
+import tempfile
+import zipfile
 import gradio as gr
-import re, os, zipfile, tempfile
+from huggingface_hub import hf_hub_download
 
-def parse_ai_response(text):
-    files = []
-    pattern = r"```([a-zA-Z0-9]+)?\n([\s\S]*?)```"
-    matches = re.findall(pattern, text)
+# ---- LLM: llama.cpp via llama_cpp_agent ----
+from llama_cpp import Llama
+from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
+from llama_cpp_agent.providers import LlamaCppPythonProvider
 
+# ----------------------
+# Model configuration
+# ----------------------
+# You can change these defaults in the UI
+DEFAULT_REPO_ID = "tHottie/NeuralDaredevil-8B-abliterated-Q4_K_M-GGUF"
+DEFAULT_FILENAME = "neuraldaredevil-8b-abliterated-q4_k_m-imat.gguf"
+MODELS_DIR = "models"
+
+os.makedirs(MODELS_DIR, exist_ok=True)
+
+def ensure_model(repo_id: str, filename: str) -> str:
+    local_path = os.path.join(MODELS_DIR, filename)
+    if not os.path.exists(local_path):
+        hf_hub_download(repo_id=repo_id, filename=filename, local_dir=MODELS_DIR)
+    return local_path
+
+def build_agent(model_path: str, n_ctx=8192, n_gpu_layers=81, n_batch=1024, flash_attn=True):
+    llm = Llama(
+        model_path=model_path,
+        n_ctx=n_ctx,
+        n_gpu_layers=n_gpu_layers,
+        n_batch=n_batch,
+        flash_attn=flash_attn,
+    )
+    provider = LlamaCppPythonProvider(llm)
+    agent = LlamaCppAgent(
+        provider,
+        system_prompt=(
+            "You are an expert code-packager and software project compiler. "
+            "Given an AI-generated project description containing code blocks and hints "
+            "about filenames or structure, extract each file with its most likely filename "
+            "and exact code content. Return ONLY a strict JSON array named manifest, "
+            "where each element is an object with keys 'filename' and 'content'. "
+            "Do not add commentary outside JSON. "
+            "Ensure filenames include directories if implied (e.g., 'src/main.py'). "
+            "Preserve code exactly as provided inside code fences."
+        ),
+        predefined_messages_formatter_type=MessagesFormatterType.GEMMA_2,
+        debug_output=False
+    )
+    return agent, provider
+
+JSON_FALLBACK_NAME = "project.txt"
+
+def call_llm_manifest(agent, provider, text, temperature=0.2, top_p=0.9, top_k=40, repeat_penalty=1.1, max_tokens=2048):
+    # Instruction prompt. Model must respond with STRICT JSON.
+    prompt = f"""
+Read the following AI project description and return ONLY JSON. 
+Output schema (strict):
+[{{"filename": "server.js", "content": "// code..."}}]
+
+AI project description:
+{text}
+"""
+    settings = provider.get_provider_default_settings()
+    settings.temperature = float(temperature)
+    settings.top_p = float(top_p)
+    settings.top_k = int(top_k)
+    settings.repeat_penalty = float(repeat_penalty)
+    settings.max_tokens = int(max_tokens)
+    settings.stream = False
+
+    out = agent.get_chat_response(prompt, llm_sampling_settings=settings, print_output=False)
+    # Try to extract a JSON array from the output robustly
+    json_text = None
+    try:
+        # Prefer the largest bracketed array slice
+        start = out.find('[')
+        end = out.rfind(']')
+        if start != -1 and end != -1 and end > start:
+            json_text = out[start:end+1]
+            manifest = json.loads(json_text)
+        else:
+            raise ValueError("No JSON array found")
+    except Exception:
+        # Fallback: single-file package of raw output for transparency
+        manifest = [ {"filename": JSON_FALLBACK_NAME, "content": out} ]
+    return manifest
+
+def naive_regex_merge(text):
+    """
+    Heuristic backup that maps code fences to probable filenames by scanning nearby lines.
+    This runs only when the model output is a single fallback file OR user ticks 'Force Heuristic Merge'.
+    """
+    blocks = []
+    # Find all triple-backtick code blocks
+    code_pattern = re.compile(r"```([a-zA-Z0-9]*)\n(.*?)```", re.DOTALL)
+    # Find filename candidates in preceding lines such as '### STEP: server.js' or '`server.js`'
     lines = text.splitlines()
-    inferred_names = []
+    candidates = []
     for i, line in enumerate(lines):
-        if re.search(r"(`.*?`|\.js|\.html|\.css|\.py)", line):
-            m = re.search(r"([A-Za-z0-9_\-]+\.[A-Za-z0-9]+)", line)
-            if m:
-                inferred_names.append(m.group(1))
+        m = re.search(r"([A-Za-z0-9_\\-./]+?\\.[A-Za-z0-9]+)", line)
+        if m:
+            candidates.append(m.group(1))
 
-    for i, (lang, code) in enumerate(matches):
-        filename = inferred_names[i] if i < len(inferred_names) else f"file_{i+1}.{lang or 'txt'}"
-        files.append((filename, code))
-    return files
+    for idx, m in enumerate(code_pattern.finditer(text)):
+        lang = m.group(1) or "txt"
+        code = m.group(2)
+        filename = candidates[idx] if idx < len(candidates) else f"file_{idx+1}.{lang}"
+        blocks.append({"filename": filename, "content": code})
+    return blocks
 
-def create_zip(files):
+def create_zip_from_manifest(manifest):
     temp_dir = tempfile.mkdtemp()
     zip_path = os.path.join(temp_dir, "project.zip")
-    with zipfile.ZipFile(zip_path, "w") as zipf:
-        for name, content in files:
-            file_path = os.path.join(temp_dir, name)
-            os.makedirs(os.path.dirname(file_path), exist_ok=True)
-            with open(file_path, "w", encoding="utf-8") as f:
+    with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as z:
+        for item in manifest:
+            fname = item.get("filename", JSON_FALLBACK_NAME).lstrip("/")
+            content = item.get("content", "")
+            fpath = os.path.join(temp_dir, fname)
+            os.makedirs(os.path.dirname(fpath), exist_ok=True)
+            with open(fpath, "w", encoding="utf-8") as f:
                 f.write(content)
-            zipf.write(file_path, arcname=name)
+            z.write(fpath, arcname=fname)
     return zip_path
 
-def process_response(text):
-    files = parse_ai_response(text)
-    zip_path = create_zip(files)
+def package_with_llm(ai_text, repo_id, filename, temperature, top_p, top_k, repeat_penalty, max_tokens, force_heuristic):
+    model_path = ensure_model(repo_id, filename)
+    agent, provider = build_agent(model_path=model_path)
+
+    manifest = call_llm_manifest(
+        agent, provider, ai_text,
+        temperature=temperature, top_p=top_p, top_k=top_k,
+        repeat_penalty=repeat_penalty, max_tokens=max_tokens
+    )
+
+    # If model failed to JSON-ify properly (single fallback) or user forces merge, try heuristic merge
+    if force_heuristic or (len(manifest) == 1 and manifest[0]["filename"] == JSON_FALLBACK_NAME):
+        heuristic = naive_regex_merge(ai_text)
+        if heuristic:
+            manifest = heuristic
+
+    zip_path = create_zip_from_manifest(manifest)
     return zip_path
 
-interface = gr.Interface(
-    fn=process_response,
-    inputs=gr.Textbox(lines=30, label="Paste AI project response here"),
-    outputs=gr.File(label="Download generated ZIP"),
-    title="AI Project Packager",
-    description="Paste an AI-generated project (like a Node.js or Python app) and this tool will parse, assemble, and zip the files for you."
-)
+with gr.Blocks(title="AI Project Packager (GGUF, llama.cpp)") as demo:
+    gr.Markdown("# AI Project Packager (GGUF, llama.cpp)")
+    gr.Markdown("Paste an AI-generated multi-file project description. A local GGUF model will infer filenames and contents, then return a downloadable ZIP.")
+
+    with gr.Row():
+        ai_text = gr.Textbox(lines=24, label="Paste AI response here")
+
+    with gr.Accordion("LLM Settings", open=False):
+        repo_id = gr.Textbox(value=DEFAULT_REPO_ID, label="Model repo_id")
+        filename = gr.Textbox(value=DEFAULT_FILENAME, label="Model filename (*.gguf)")
+        with gr.Row():
+            temperature = gr.Slider(0.0, 2.0, value=0.2, step=0.05, label="Temperature")
+            top_p = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p")
+            top_k = gr.Slider(0, 100, value=40, step=1, label="Top-k")
+        with gr.Row():
+            repeat_penalty = gr.Slider(0.8, 2.0, value=1.1, step=0.05, label="Repeat penalty")
+            max_tokens = gr.Slider(256, 4096, value=2048, step=32, label="Max tokens")
+        force_heuristic = gr.Checkbox(value=False, label="Force heuristic filename/code merge if JSON parse fails")
+
+    out_zip = gr.File(label="Download packaged ZIP")
+
+    run_btn = gr.Button("Package Project", variant="primary")
+    run_btn.click(
+        fn=package_with_llm,
+        inputs=[ai_text, repo_id, filename, temperature, top_p, top_k, repeat_penalty, max_tokens, force_heuristic],
+        outputs=[out_zip]
+    )
 
 if __name__ == "__main__":
-    interface.launch()
+    demo.launch()

requirements.txt

@@ -1 +1,4 @@
-gradio>=4.0
+gradio==5.49.1
+huggingface_hub>=0.24.0
+llama-cpp-python>=0.2.90
+llama-cpp-agent>=0.2.43

space.yaml

@@ -1,10 +1,9 @@
-# space.yaml
 title: AI Project Packager
 emoji: 📦
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 4.0
+sdk_version: 5.49.1
 app_file: app.py
 pinned: false
 license: apache-2.0