Update app.py

app.py

@@ -1,64 +1,164 @@
+# main.py
+
+import os
+import json
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+
+from google import genai
+from google.genai import types
+from google.genai.types import Tool, GoogleSearch
+from huggingface_hub import create_repo, snapshot_upload
+
+# ——— Configuration ———
+MODEL_ID = "gemini-2.5-flash-preview-04-17"
+WORKSPACE_DIR = "workspace"
+SYSTEM_INSTRUCTION = (
+    "You are a helpful coding assistant that scaffolds a complete Hugging Face Space app. "
+    "Based on the user's request, decide between Gradio or Streamlit (whichever fits best), "
+    "and respond with exactly one JSON object with keys:\n"
+    "  • \"framework\": either \"gradio\" or \"streamlit\"\n"
+    "  • \"files\": a map of relative file paths to file contents\n"
+    "  • \"message\": a human-readable summary\n"
+    "Do not include extra text or markdown."
 )
 
 
-if __name__ == "__main__":
+def start_app(gemini_key, hf_token, hf_username, repo_name):
+    """
+    Initialize workspace and Gemini chat state.
+    """
+    os.makedirs(WORKSPACE_DIR, exist_ok=True)
+    client = genai.Client(api_key=gemini_key)
+    config = types.GenerateContentConfig(system_instruction=SYSTEM_INSTRUCTION)
+    tools = [Tool(google_search=GoogleSearch())]
+    chat = client.chats.create(model=MODEL_ID, config=config, tools=tools)
+
+    local_path = os.path.join(WORKSPACE_DIR, repo_name)
+    os.makedirs(local_path, exist_ok=True)
+
+    state = {
+        "chat": chat,
+        "hf_token": hf_token,
+        "hf_username": hf_username,
+        "repo_name": repo_name,
+        "created": False,
+        "repo_id": None,
+        "local_path": local_path,
+        "logs": [f"Initialized workspace at {WORKSPACE_DIR}/{repo_name}."],
+    }
+    return state
+
+
+def handle_message(user_msg, state):
+    """
+    Send user message to Gemini, apply updates, commit to HF, and log steps.
+    """
+    chat = state["chat"]
+    logs = state.get("logs", [])
+    logs.append(f"> User: {user_msg}")
+
+    # Generate or debug code via Gemini
+    resp = chat.send_message(user_msg)
+    logs.append("Received response from Gemini.")
+    text = resp.text
+
+    try:
+        data = json.loads(text)
+        framework = data["framework"]
+        files = data.get("files", {})
+        reply_msg = data.get("message", "")
+    except Exception:
+        logs.append("⚠️ Failed to parse assistant JSON.\n" + text)
+        state["logs"] = logs
+        return "⚠️ Parsing error. Check logs.", state
+
+    # On first structured response, create the HF Space
+    if not state["created"]:
+        full_repo = f"{state['hf_username']}/{state['repo_name']}"
+        logs.append(f"Creating HF Space '{full_repo}' with template '{framework}'.")
+        create_repo(
+            repo_id=full_repo,
+            token=state["hf_token"],
+            exist_ok=True,
+            repo_type="space",
+            space_sdk=framework
+        )
+        state["created"] = True
+        state["repo_id"] = full_repo
+        state["embed_url"] = f"https://huggingface.co/spaces/{full_repo}"
+
+    # Write file updates
+    if files:
+        logs.append(f"Writing {len(files)} file(s): {list(files.keys())}")
+        for relpath, content in files.items():
+            dest = os.path.join(state["local_path"], relpath)
+            os.makedirs(os.path.dirname(dest), exist_ok=True)
+            with open(dest, "w", encoding="utf-8") as f:
+                f.write(content)
+
+    # Commit the snapshot
+    logs.append("Uploading snapshot to Hugging Face...")
+    snapshot_upload(
+        repo_id=state["repo_id"],
+        repo_type="space",
+        token=state["hf_token"],
+        folder=state["local_path"],
+        commit_message="Update from assistant"
+    )
+    logs.append("Snapshot upload complete.")
+
+    state["logs"] = logs
+    return reply_msg, state
+
+
+# ——— Gradio UI ———
+with gr.Blocks() as demo:
+    with gr.Sidebar():
+        gemini_key = gr.Textbox(label="Gemini API Key", type="password")
+        hf_token   = gr.Textbox(label="Hugging Face Token", type="password")
+        hf_user    = gr.Textbox(label="HF Username")
+        repo_name  = gr.Textbox(label="New App (repo) name")
+        start_btn  = gr.Button("Start a new app")
+
+    chatbot = gr.Chatbot()
+    state   = gr.State(value=None)
+    logs_display = gr.Textbox(label="Operation Logs", interactive=False, lines=8)
+    preview_iframe = gr.HTML("<p>No deployed app yet.</p>")
+
+    user_msg = gr.Textbox(label="Your message")
+    send_btn = gr.Button("Send")
+
+    def on_start(g_key, h_token, h_user, r_name):
+        s = start_app(g_key, h_token, h_user, r_name)
+        logs = "\n".join(s["logs"])
+        return s, logs, "<p>Awaiting first instruction...</p>"
+
+    start_btn.click(
+        on_start,
+        inputs=[gemini_key, hf_token, hf_user, repo_name],
+        outputs=[state, logs_display, preview_iframe]
+    )
+
+    def on_send(msg, chat_history, s):
+        if s is None:
+            return chat_history, s, "", ""
+        reply, new_state = handle_message(msg, s)
+        chat_history = chat_history + [(msg, reply)]
+        logs = "\n".join(new_state.get("logs", []))
+        embed = ""
+        if new_state.get("embed_url"):
+            embed = f'<iframe src="{new_state["embed_url"]}" width="100%" height="500px"></iframe>'
+        return chat_history, new_state, logs, embed
+
+    send_btn.click(
+        on_send,
+        inputs=[user_msg, chatbot, state],
+        outputs=[chatbot, state, logs_display, preview_iframe]
+    )
+    user_msg.submit(
+        on_send,
+        inputs=[user_msg, chatbot, state],
+        outputs=[chatbot, state, logs_display, preview_iframe]
+    )
+
     demo.launch()