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Update app.py
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app.py
CHANGED
@@ -2,8 +2,7 @@ import gradio as gr
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from PIL import Image
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from inference.main import MultiModalPhi2
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multimodal_phi2 = MultiModalPhi2(
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modelname_or_path="Navyabhat/Llava-Phi2",
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temperature=0.2,
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@@ -11,7 +10,38 @@ multimodal_phi2 = MultiModalPhi2(
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device="cpu",
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)
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def add_content(chatbot, text, image, audio_upload, audio_mic) -> gr.Chatbot:
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textflag, imageflag, audioflag = False, False, False
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if text not in ["", None]:
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@@ -29,14 +59,10 @@ def add_content(chatbot, text, image, audio_upload, audio_mic) -> gr.Chatbot:
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audioflag = True
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if not any([textflag, imageflag, audioflag]):
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# Raise an error if neither text nor file is provided
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raise gr.Error("Enter a valid text, image or audio")
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return chatbot
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def clear_data():
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return {prompt: None, image: None, audio_upload: None, audio_mic: None, chatbot: []}
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def run(history, text, image, audio_upload, audio_mic):
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if text in [None, ""]:
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text = None
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@@ -55,61 +81,10 @@ def run(history, text, image, audio_upload, audio_mic):
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if image is not None:
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image = Image.open(image)
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outputs = multimodal_phi2(text, audio, image)
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# outputs = ""
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history.append((None, outputs.title()))
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return history, None, None, None, None
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gr.Markdown("## MulitModal Phi2 Model Pretraining and Finetuning from Scratch")
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with gr.Row():
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with gr.Column(scale=4):
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with gr.Box():
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with gr.Row():
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# Adding a Textbox with a placeholder "write prompt"
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prompt = gr.Textbox(
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placeholder="Ask anything", lines=2, label="Query", value=None
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)
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with gr.Row():
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# Adding image
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image = gr.Image(type="filepath", value=None)
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with gr.Row():
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# Add audio
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audio_upload = gr.Audio(source="upload", type="filepath")
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audio_mic = gr.Audio(
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source="microphone", type="filepath", format="mp3"
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)
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with gr.Column(scale=8):
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with gr.Box():
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with gr.Row():
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chatbot = gr.Chatbot(
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avatar_images=("🧑", "🤖"),
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height=550,
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)
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with gr.Row():
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# Adding a Button
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submit = gr.Button()
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clear = gr.Button(value="Clear")
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submit.click(
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add_content,
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inputs=[chatbot, prompt, image, audio_upload, audio_mic],
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outputs=[chatbot],
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).success(
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run,
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inputs=[chatbot, prompt, image, audio_upload, audio_mic],
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outputs=[chatbot, prompt, image, audio_upload, audio_mic],
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)
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clear.click(
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clear_data,
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outputs=[prompt, image, audio_upload, audio_mic, chatbot],
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)
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demo.launch()
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from PIL import Image
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from inference.main import MultiModalPhi2
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# Initialize the chatbot model
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multimodal_phi2 = MultiModalPhi2(
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modelname_or_path="Navyabhat/Llava-Phi2",
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temperature=0.2,
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device="cpu",
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)
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# Initialize chatbot history
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messages = []
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# UI setup
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with gr.Blocks() as demo:
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chatbot = gr.Chatbot(
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[],
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elem_id="chatbot",
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bubble_full_width=False,
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avatar_images=(None, None), # You can specify avatar images if needed
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)
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with gr.Row():
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txt = gr.Textbox(
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scale=4,
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show_label=False,
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placeholder="Enter text and press enter, or upload an image",
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container=False,
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)
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btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
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txt_msg = txt.submit(add_content, [chatbot, txt], [chatbot, txt], queue=False).then(
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run, [chatbot, txt, None, None, None], [chatbot, txt, None, None, None], api_name="bot_response"
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)
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txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
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file_msg = btn.upload(add_content, [chatbot, None, None, btn, None], [chatbot, None, None, None], queue=False).then(
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run, [chatbot, None, None, None, btn], [chatbot, None, None, None, None]
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)
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chatbot.like(print_like_dislike, None, None)
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# Function to add content to chatbot
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def add_content(chatbot, text, image, audio_upload, audio_mic) -> gr.Chatbot:
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textflag, imageflag, audioflag = False, False, False
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if text not in ["", None]:
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audioflag = True
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if not any([textflag, imageflag, audioflag]):
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# Raise an error if neither text nor file is provided
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raise gr.Error("Enter a valid text, image, or audio")
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return chatbot
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# Function to run the chatbot
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def run(history, text, image, audio_upload, audio_mic):
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if text in [None, ""]:
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text = None
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if image is not None:
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image = Image.open(image)
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outputs = multimodal_phi2(text, audio, image)
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history.append((None, outputs.title()))
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return history, None, None, None, None
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# Launch the Gradio UI
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demo.queue()
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demo.launch()
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