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README.md

@@ -1,12 +1,12 @@
+
 ---
-title: Chatinterface Thoughts Main
-emoji: 🌖
-colorFrom: blue
-colorTo: blue
+title: chatinterface_thoughts_main 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
 sdk_version: 5.11.0
-app_file: app.py
+app_file: run.py
 pinned: false
+hf_oauth: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1,2 @@
+gradio-client @ git+https://github.com/gradio-app/gradio@be40307d1d11421e01bf91fa5e05ec4ab97b09d8#subdirectory=client/python
+https://gradio-pypi-previews.s3.amazonaws.com/be40307d1d11421e01bf91fa5e05ec4ab97b09d8/gradio-5.11.0-py3-none-any.whl

run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatinterface_thoughts"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from gradio import ChatMessage\n", "import time\n", "\n", "sleep_time = 0.5\n", "\n", "def simulate_thinking_chat(message, history):\n", "    start_time = time.time()\n", "    response = ChatMessage(\n", "        content=\"\",\n", "        metadata={\"title\": \"_Thinking_ step-by-step\", \"id\": 0, \"status\": \"pending\"}\n", "    )\n", "    yield response\n", "\n", "    thoughts = [\n", "        \"First, I need to understand the core aspects of the query...\",\n", "        \"Now, considering the broader context and implications...\",\n", "        \"Analyzing potential approaches to formulate a comprehensive answer...\",\n", "        \"Finally, structuring the response for clarity and completeness...\"\n", "    ]\n", "\n", "    accumulated_thoughts = \"\"\n", "    for thought in thoughts:\n", "        time.sleep(sleep_time)\n", "        accumulated_thoughts += f\"- {thought}\\n\\n\"\n", "        response.content = accumulated_thoughts.strip()\n", "        yield response\n", "\n", "    response.metadata[\"status\"] = \"done\"\n", "    response.metadata[\"duration\"] = time.time() - start_time\n", "    yield response\n", "\n", "    response = [\n", "        response,\n", "        ChatMessage(\n", "            content=\"Based on my thoughts and analysis above, my response is: This dummy repro shows how thoughts of a thinking LLM can be progressively shown before providing its final answer.\"\n", "        )\n", "    ]\n", "    yield response\n", "\n", "\n", "demo = gr.ChatInterface(\n", "    simulate_thinking_chat,\n", "    title=\"Thinking LLM Chat Interface \ud83e\udd14\",\n", "    type=\"messages\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

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+import gradio as gr
+from gradio import ChatMessage
+import time
+
+sleep_time = 0.5
+
+def simulate_thinking_chat(message, history):
+    start_time = time.time()
+    response = ChatMessage(
+        content="",
+        metadata={"title": "_Thinking_ step-by-step", "id": 0, "status": "pending"}
+    )
+    yield response
+
+    thoughts = [
+        "First, I need to understand the core aspects of the query...",
+        "Now, considering the broader context and implications...",
+        "Analyzing potential approaches to formulate a comprehensive answer...",
+        "Finally, structuring the response for clarity and completeness..."
+    ]
+
+    accumulated_thoughts = ""
+    for thought in thoughts:
+        time.sleep(sleep_time)
+        accumulated_thoughts += f"- {thought}\n\n"
+        response.content = accumulated_thoughts.strip()
+        yield response
+
+    response.metadata["status"] = "done"
+    response.metadata["duration"] = time.time() - start_time
+    yield response
+
+    response = [
+        response,
+        ChatMessage(
+            content="Based on my thoughts and analysis above, my response is: This dummy repro shows how thoughts of a thinking LLM can be progressively shown before providing its final answer."
+        )
+    ]
+    yield response
+
+
+demo = gr.ChatInterface(
+    simulate_thinking_chat,
+    title="Thinking LLM Chat Interface 🤔",
+    type="messages",
+)
+
+if __name__ == "__main__":
+    demo.launch()