Update app.py

app.py

@@ -1,46 +1,61 @@
 import gradio as gr
-import time
-
-# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.
-
-def add_text(history, text):
-    history = history + [(text, None)]
-    return history, gr.update(value="", interactive=False)
-
-
-def add_file(history, file):
-    history = history + [((file.name,), None)]
-    return history
-
-
-def bot(history):
-    response = "**That's cool!**"
-    history[-1][1] = ""
-    for character in response:
-        history[-1][1] += character
-        time.sleep(0.05)
-        yield history
-
-
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot([], elem_id="chatbot").style(height=750)
-
-    with gr.Row():
-        with gr.Column(scale=0.85):
-            txt = gr.Textbox(
-                show_label=False,
-                placeholder="Enter text and press enter, or upload an image",
-            ).style(container=False)
-        with gr.Column(scale=0.15, min_width=0):
-            btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
-
-    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
-        bot, chatbot, chatbot
-    )
-    txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False)
-    file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(
-        bot, chatbot, chatbot
-    )
-
-demo.queue()
-demo.launch()
+from gradio.mix import Parallel, Series
+from gradio.blocks import Input, Output, Processor
+from PIL import Image
+import speech_recognition as sr
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+# Load pre-trained model and tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained("gpt2")
+
+# Placeholder function to handle text input
+def handle_text(text):
+    # encode the new user input, add the eos_token and return a tensor in Pytorch
+    new_user_input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors='pt')
+
+    # append the new user input tokens to the chat history
+    bot_input_ids = new_user_input_ids
+
+    # generate a response
+    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+
+    # Print the generated chat
+    chat_output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return chat_output
+
+# Placeholder function to handle image input
+def handle_image(img):
+    # This is a placeholder function, replace with your own image processing function
+    return "This image seems nice!"
+
+# Placeholder function to handle audio input
+def handle_audio(audio):
+    # This is a placeholder function, replace with your own audio processing function
+    r = sr.Recognizer()
+    with sr.AudioFile(audio.name) as source:
+        audio_data = r.record(source)
+        text = r.recognize_google(audio_data)
+        return handle_text(text)
+
+# Define the Gradio interface
+iface = gr.Interface(
+    [
+        Parallel(
+            Input(type="text", label="Input Text"),
+            Input(type="image", label="Upload Image"),
+            Input(type="audio", label="Input Audio"),
+        ),
+        Series(
+            Processor(handle_text, "text"),
+            Processor(handle_image, "image"),
+            Processor(handle_audio, "audio"),
+            Output(type="text", label="Output"),
+        )
+    ],
+    title="Multimodal Chatbot",
+    description="This chatbot can handle text, image, and audio inputs. Try it out!",
+)
+
+# Launch the Gradio interface
+iface.launch()