released bot

app.py

@@ -1,63 +1,112 @@
 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)",
-        ),
-    ],
+
+# Import modules from other files
+from bot import chatbot, model_inference, BOT_AVATAR, EXAMPLES, model_selector, decoding_strategy, temperature, max_new_tokens, repetition_penalty, top_p
+from voice import respond
+from chat import videochat
+
+# Define Gradio theme
+theme = gr.themes.Soft(
+    primary_hue="blue",
+    secondary_hue="orange",
+    neutral_hue="gray",
+    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif']
+).set(
+    body_background_fill_dark="#111111",
+    block_background_fill_dark="#111111",
+    block_border_width="1px",
+    block_title_background_fill_dark="#1e1c26",
+    input_background_fill_dark="#292733",
+    button_secondary_background_fill_dark="#24212b",
+    border_color_primary_dark="#343140",
+    background_fill_secondary_dark="#111111",
+    color_accent_soft_dark="transparent"
 )
 
 
-if __name__ == "__main__":
-    demo.launch()
+# Chat interface block
+with gr.Blocks(
+        fill_height=True,
+        css=""".gradio-container .avatar-container {height: 40px width: 40px !important;} #duplicate-button {margin: auto; color: white; background: #f1a139; border-radius: 100vh; margin-top: 2px; margin-bottom: 2px;}""",
+) as chat:
+    gr.Markdown("# Image Chat, Image Generation, Image classification and Normal Chat")
+    with gr.Row(elem_id="model_selector_row"):
+        # model_selector defined in chatbot.py
+        pass  
+    # decoding_strategy, temperature, top_p defined in chatbot.py
+    decoding_strategy.change(
+        fn=lambda selection: gr.Slider(
+            visible=(
+                    selection
+                    in [
+                        "contrastive_sampling",
+                        "beam_sampling",
+                        "Top P Sampling",
+                        "sampling_top_k",
+                    ]
+            )
+        ),
+        inputs=decoding_strategy,
+        outputs=temperature,
+    )
+    decoding_strategy.change(
+        fn=lambda selection: gr.Slider(visible=(selection in ["Top P Sampling"])),
+        inputs=decoding_strategy,
+        outputs=top_p,
+    )
+    gr.ChatInterface(
+        fn=model_inference,
+        chatbot=chatbot,
+        examples=EXAMPLES,
+        multimodal=True,
+        cache_examples=False,
+        additional_inputs=[
+            model_selector,
+            decoding_strategy,
+            temperature,
+            max_new_tokens,
+            repetition_penalty,
+            top_p,
+            gr.Checkbox(label="Web Search", value=True),
+        ],
+    )
+
+# Voice chat block
+with gr.Blocks() as voice:
+    with gr.Row():
+        select = gr.Dropdown(['Nous Hermes Mixtral 8x7B DPO', 'Mixtral 8x7B', 'StarChat2 15b', 'Mistral 7B v0.3',
+                              'Phi 3 mini', 'Zephyr 7b'], value="Mistral 7B v0.3", label="Select Model")
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=999999,
+            step=1,
+            value=0,
+            visible=False
+        )
+        input = gr.Audio(label="User", sources="microphone", type="filepath", waveform_options=False)
+        output = gr.Audio(label="AI", type="filepath",
+                          interactive=False,
+                          autoplay=True,
+                          elem_classes="audio")
+        gr.Interface(
+            fn=respond,
+            inputs=[input, select, seed],
+            outputs=[output], api_name="translate", live=True)
+
+# Live chat block
+with gr.Blocks() as livechat:
+    gr.Interface(
+        fn=videochat,
+        inputs=[gr.Image(type="pil",sources="webcam", label="Upload Image"), gr.Textbox(label="Prompt", value="what he is doing")],
+        outputs=gr.Textbox(label="Answer")
+    )
+
+
+# Main application block
+with gr.Blocks(theme=theme, title="Open Source GPT-4o") as demo:
+    gr.Markdown("# GPT-4o")
+    gr.TabbedInterface([chat, voice, livechat], ['💬 SuperChat','🗣️ Voice Chat','📸 Live Chat'])
+
+demo.queue(max_size=300)
+demo.launch()