Update app.py

app.py

@@ -1,63 +1,59 @@
 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 os
+
+examples = [
+    {"text": "What is this image?", "files": ["sample/ducky.jpg"]},
+    {"text": "Can you summarize and translate this?", "files": ["sample/sermon.mp4"]}
+]
+
+# Function to print the like/dislike data
+def print_like_dislike(x: gr.LikeData):
+    print(x.index, x.value, x.liked)
+
+# Function to add a message to the chat history
+def add_message(history, message):
+    for x in message["files"]:
+        history.append(((x,), None))
+    if message["text"] is not None:
+        history.append((message["text"], None))
+    return history, gr.MultimodalTextbox(value=None, interactive=False), message["text"]
+
+# Function to generate the bot's response
+def bot(history, user_input):
+    if history:
+        history[-1][1] = f"Cool! You said: {user_input}"
+    return history
+
+def main():
+    # Define the Gradio interface using Blocks
+    with gr.Blocks(fill_height=True) as webApp:
+        chatbot = gr.Chatbot(
+            elem_id="chatbot",
+            bubble_full_width=False,
+            scale=1,
+        )
+        chat_input = gr.MultimodalTextbox(interactive=True,
+                                          file_count="multiple",
+                                          placeholder="Enter message or upload file...", 
+                                          show_label=False)
+        user_input = gr.State()
+        chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input, user_input])
+        bot_msg = chat_msg.then(bot, [chatbot, user_input], chatbot, api_name="bot_response")
+        bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
+        chatbot.like(print_like_dislike, None, None)
+
+        """
+        # Add examples
+        gr.Examples(
+            examples=examples,
+            inputs=chat_input,
+            label="Click on an example to try it"
+        )
+        """
+
+    # Start the queue and launch the interface
+    webApp.queue()
+    webApp.launch(share=True)
 
 if __name__ == "__main__":
-    demo.launch()
+    main()