initial commit

.gitignore

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+venv/
+node_modules/
+*.log
+
+# Python
+__pycache__/
+*.pyc
+
+# Env
+.env
+
+# VS Code / Visual Studio
+.vs/
+.vscode/
+
+# OS
+.DS_Store
+Thumbs.db

app.py

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+
+# ./app.py
+
+"""
+The Interface Skeleton - The code sets up the navigation panel and the multimodal chat interface
+"""
+
+import gradio as gr
+from core_logic import chat_function
+from storage import save_chat, load_history
+
+with gr.Blocks(css="styles.css", theme=gr.themes.Soft()) as demo:
+    # State variables for persistence
+    current_chat_id = gr.State("")
+    session_history = gr.State([])
+
+    with gr.Row():
+        # --- SIDEBAR (Navigation Panel) ---
+        with gr.Column(scale=1, variant="secondary"):
+            gr.Markdown("## 🛠️ Agent Architect")
+            new_btn = gr.Button("➕ New Chat", variant="primary")
+            
+            gr.Markdown("### 🕒 Recent Conversations")
+            # This would be populated from storage.py
+            history_list = gr.Dataset(
+                components=[gr.Textbox(visible=False)], 
+                label="", 
+                samples=load_history()
+            )
+            
+            gr.Markdown("---")
+            download_format = gr.Radio([".txt", ".pdf", ".docx"], label="Export Format")
+            export_btn = gr.Button("📥 Download Current Chat")
+
+        # --- MAIN CHAT INTERFACE ---
+        with gr.Column(scale=4):
+            chatbot = gr.Chatbot(
+                show_label=False, 
+                elem_id="chat-window", 
+                height=750,
+                type="messages",
+                multimodal=True
+            )
+            
+            with gr.Row():
+                user_input = gr.MultimodalTextbox(
+                    show_label=False,
+                    placeholder="Describe your agentic architecture or upload code...",
+                    file_types=[".py", ".pdf", ".js", ".md", ".txt", ".docx"],
+                    file_count="multiple"
+                )
+
+    # --- EVENT HANDLERS ---
+    def handle_user_msg(message, history):
+        # This will link to our core_logic.py
+        return chat_function(message, history)
+
+    user_input.submit(handle_user_msg, [user_input, chatbot], [user_input, chatbot])
+    
+demo.launch()

core_logic.py

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+
+# ./core_logic.py
+
+"""
+The Inference Engine - Where the "Technical Genius" persona lives. It uses the huggingface_hub InferenceClient to run the model without local CPU strain
+"""
+
+import os
+from huggingface_hub import InferenceClient
+from tools import web_search, parse_file
+
+# Recommended: Qwen2.5-Coder-32B or Llama-3.1-70B-Instruct
+#client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct", token=os.getenv("HF_TOKEN"))
+client = InferenceClient("deepseek-ai/DeepSeek-V4-Pro", token=os.getenv("HF_TOKEN"))
+
+SYSTEM_PROMPT = """
+You are the 'Silicon Architect'�a master-stroke creative genius in AI Engineering and Technical Architecture. 
+Your goal is to provide production-grade, highly optimized solutions for web and mobile AI applications.
+
+CORE DIRECTIVES:
+1. ARCHITECTURAL RIGOR: Always consider scalability, async patterns, and state management.
+2. AGENTIC EXPERTISE: You understand recurrent-depth simulations, tool-calling, and autonomous loops.
+3. CODE QUALITY: Write clean, PEP 8 compliant, and secure Python/JS code.
+4. INNOVATION: Suggest the latest libraries and frameworks (FastAPI, LangGraph, Pydantic AI).
+5. RESEARCH: If the user asks about new tech, use your Web Search capability to provide factual, up-to-date documentation.
+
+When a user provides files, analyze the code structure and logic before proposing changes.
+"""
+
+def chat_function(message, history):
+    # 1. Handle Multimodal Input (Files)
+    files_content = ""
+    user_text = message["text"]
+    
+    if message["files"]:
+        for file_data in message["files"]:
+            # Gradio file objects have a .path attribute
+            files_content += parse_file(file_data)
+    
+    # 2. Logic for "Web Search" trigger
+    # A simple keyword trigger or the LLM can decide. For now, we look for 'search' or 'docs'.
+    if "search" in user_text.lower() or "documentation" in user_text.lower():
+        search_query = user_text.replace("search", "").strip()
+        web_results = web_search(search_query)
+        full_user_input = f"RESEARCH RESULTS:\n{web_results}\n\nUSER FILES:\n{files_content}\n\nUSER QUESTION: {user_text}"
+    else:
+        full_user_input = f"USER FILES:\n{files_content}\n\nUSER QUESTION: {user_text}"
+
+    # 3. Construct Messages for Inference
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+    for turn in history:
+        messages.append({"role": "user", "content": turn[0]})
+        messages.append({"role": "assistant", "content": turn[1]})
+    
+    messages.append({"role": "user", "content": full_user_input})
+
+    # 4. Stream Response
+    response = ""
+    for message_chunk in client.chat_completion(
+        messages,
+        max_tokens=2048,
+        stream=True,
+        temperature=0.2, # Low temperature for high technical precision
+    ):
+        token = message_chunk.choices[0].delta.content
+        if token:
+            response += token
+            yield response

requirements.txt

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+gradio
+huggingface_hub
+python-dotenv
+duckduckgo-search
+pypdf
+python-docx
+pandas

storage.py

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+
+# ./storage.py
+
+"""
+Persistence Layer - Handles the "Save/Load" functionality using Hugging Face Dataset as a database
+"""
+
+import json
+import os
+from huggingface_hub import HfApi, hf_hub_download
+
+REPO_ID = "your-username/your-private-dataset" # Update this
+api = HfApi(token=os.getenv("HF_TOKEN"))
+
+def save_chat(chat_id, history):
+    """Save chat history as a JSON file to HF Dataset."""
+    file_path = f"{chat_id}.json"
+    with open(file_path, "w") as f:
+        json.dump(history, f)
+    
+    api.upload_file(
+        path_or_fileobj=file_path,
+        path_in_repo=f"chats/{file_path}",
+        repo_id=REPO_ID,
+        repo_type="dataset"
+    )
+
+def load_history():
+    """Retrieve all past chat IDs for the sidebar."""
+    try:
+        files = api.list_repo_files(repo_id=REPO_ID, repo_type="dataset")
+        chat_files = [f.split("/")[-1].replace(".json", "") for f in files if f.startswith("chats/")]
+        return [[f] for f in chat_files]
+    except Exception:
+        return []

styles.css

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+
+
+.sidebar {
+    background-color: #f8f9fa;
+    border-right: 1px solid #ddd;
+    height: 100vh;
+}
+
+.chatbot-container {
+    border-radius: 15px !important;
+}

tools.py

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+
+# ./tools.py
+
+"""
+The Research & Extraction Engine - The module handles "Web Search" via Tavily and the parsing of uploaded files (PDFs, Python scripts, etc.)
+"""
+
+import os
+from tavily import TavilyClient
+from pypdf import PdfReader
+import docx
+
+# Initialize Tavily
+tavily = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
+
+def web_search(query: str):
+    """Perform a technical search for documentation or latest AI trends."""
+    search_result = tavily.search(query=query, search_depth="advanced", max_results=5)
+    context = "\n".join([f"Source: {r['url']}\nContent: {r['content']}" for r in search_result['results']])
+    return context
+
+def parse_file(file_path):
+    """Extract text from various file formats for the LLM to process."""
+    ext = os.path.splitext(file_path)[-1].lower()
+    text = f"--- File: {os.path.basename(file_path)} ---\n"
+    
+    if ext == ".pdf":
+        reader = PdfReader(file_path)
+        for page in reader.pages:
+            text += page.extract_text()
+    elif ext == ".docx":
+        doc = docx.Document(file_path)
+        text += "\n".join([para.text for para in doc.paragraphs])
+    elif ext in [".py", ".txt", ".md", ".html", ".js", ".yaml", ".toml"]:
+        with open(file_path, "r", encoding="utf-8") as f:
+            text += f.read()
+    else:
+        text += "[Non-text file detected or unsupported format]"
+    
+    return text + "\n---\n"