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README.md

@@ -1,12 +1,19 @@
 ---
-title: Integrated Textbox
-emoji: 🐠
-colorFrom: green
+title: Integrated Text Box
+emoji: 📝
+colorFrom: purple
 colorTo: red
 sdk: gradio
-sdk_version: 5.33.0
+sdk_version: 5.31.0
 app_file: app.py
 pinned: false
+license: mit
+short_description: Talk or type to ANY LLM!
+tags: [webrtc, websocket, gradio, secret|HF_TOKEN]
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Integrated Textbox
+
+Talk or type to ANY LLM!
+
+

app.py

@@ -0,0 +1,143 @@
+# /// script
+# dependencies = [
+#   "fastrtc[vad, stt]==0.0.26.rc1",
+#   "openai",
+# ]
+# ///
+
+
+import gradio as gr
+import huggingface_hub
+from fastrtc import (
+    AdditionalOutputs,
+    ReplyOnPause,
+    WebRTC,
+    WebRTCData,
+    WebRTCError,
+    get_hf_turn_credentials,
+    get_stt_model,
+)
+from gradio.utils import get_space
+from openai import OpenAI
+
+stt_model = get_stt_model()
+
+conversations = {}
+
+
+def response(
+    data: WebRTCData,
+    conversation: list[dict],
+    token: str | None = None,
+    model: str = "meta-llama/Llama-3.2-3B-Instruct",
+    provider: str = "sambanova",
+):
+    print("conversation before", conversation)
+    if not provider.startswith("http") and not token:
+        raise WebRTCError("Please add your HF token.")
+
+    if data.audio is not None and data.audio[1].size > 0:
+        user_audio_text = stt_model.stt(data.audio)
+        conversation.append({"role": "user", "content": user_audio_text})
+    else:
+        conversation.append({"role": "user", "content": data.textbox})
+
+    yield AdditionalOutputs(conversation)
+
+    if provider.startswith("http"):
+        client = OpenAI(base_url=provider, api_key="ollama")
+    else:
+        client = huggingface_hub.InferenceClient(
+            api_key=token,
+            provider=provider,  # type: ignore
+        )
+
+    request = client.chat.completions.create(
+        model=model,
+        messages=conversation,  # type: ignore
+        temperature=1,
+        top_p=0.1,
+    )
+    response = {"role": "assistant", "content": request.choices[0].message.content}
+
+    conversation.append(response)
+    print("conversation after", conversation)
+    yield AdditionalOutputs(conversation)
+
+
+css = """
+footer {
+    display: none !important;
+}
+"""
+
+providers = [
+    "black-forest-labs",
+    "cerebras",
+    "cohere",
+    "fal-ai",
+    "fireworks-ai",
+    "hf-inference",
+    "hyperbolic",
+    "nebius",
+    "novita",
+    "openai",
+    "replicate",
+    "sambanova",
+    "together",
+]
+
+
+def hide_token(provider: str):
+    if provider.startswith("http"):
+        return gr.Textbox(visible=False)
+    return gr.skip()
+
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML(
+        """
+            <h1 style='text-align: center; display: flex; align-items: center; justify-content: center;'>
+            <img src="https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/AV_Huggy.png" alt="Streaming Huggy" style="height: 50px; margin-right: 10px"> FastRTC Chat
+            </h1>
+        """
+    )
+    with gr.Sidebar():
+        token = gr.Textbox(
+            placeholder="Place your HF token here", type="password", label="HF Token"
+        )
+        model = gr.Dropdown(
+            choices=["meta-llama/Llama-3.2-3B-Instruct"],
+            allow_custom_value=True,
+            label="Model",
+        )
+        provider = gr.Dropdown(
+            label="Provider",
+            choices=providers,
+            value="sambanova",
+            info="Select a hf-compatible provider or type the url of your server, e.g. http://127.0.0.1:11434/v1 for ollama",
+            allow_custom_value=True,
+        )
+    provider.change(hide_token, inputs=[provider], outputs=[token])
+    cb = gr.Chatbot(type="messages", height=600)
+    webrtc = WebRTC(
+        modality="audio",
+        mode="send",
+        variant="textbox",
+        rtc_configuration=get_hf_turn_credentials if get_space() else None,
+        server_rtc_configuration=get_hf_turn_credentials(ttl=3_600 * 24 * 30)
+        if get_space()
+        else None,
+    )
+    webrtc.stream(
+        ReplyOnPause(response),  # type: ignore
+        inputs=[webrtc, cb, token, model, provider],
+        outputs=[cb],
+        concurrency_limit=100,
+    )
+    webrtc.on_additional_outputs(
+        lambda old, new: new, inputs=[cb], outputs=[cb], concurrency_limit=100
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_port=6980)