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import gradio as gr
import requests
import base64
import tempfile
import os
from PIL import Image
import numpy as np
# ==============================
# Configuration
# ==============================
HF_TOKEN = os.getenv("HF_TOKEN") # optional but recommended
# Example Models (can be extended)
MODEL_REGISTRY = {
"Text - Mistral 7B Instruct": {
"type": "text",
"endpoint": "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
},
"Text - Llama 3 8B Instruct": {
"type": "text",
"endpoint": "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
},
"Vision - LLaVA": {
"type": "vision",
"endpoint": "https://api-inference.huggingface.co/models/llava-hf/llava-1.5-7b-hf"
},
"Audio - Whisper": {
"type": "audio",
"endpoint": "https://api-inference.huggingface.co/models/openai/whisper-large-v3"
}
}
headers = {
"Authorization": f"Bearer {HF_TOKEN}" if HF_TOKEN else None
}
# Helper Functions
def query_text_model(endpoint, prompt):
payload = {"inputs": prompt}
response = requests.post(endpoint, headers=headers, json=payload)
try:
return response.json()[0]["generated_text"]
except:
return str(response.json())
def query_vision_model(endpoint, prompt, image):
buffered = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
image.save(buffered.name)
with open(buffered.name, "rb") as f:
img_bytes = f.read()
payload = {
"inputs": {
"image": base64.b64encode(img_bytes).decode("utf-8"),
"text": prompt
}
}
response = requests.post(endpoint, headers=headers, json=payload)
return response.json()
def query_audio_model(endpoint, audio_path):
with open(audio_path, "rb") as f:
data = f.read()
response = requests.post(endpoint, headers=headers, data=data)
return response.json()
# Main Chat Function
def multimodal_chat(prompt, image, audio, selected_models, history):
outputs = {}
for model_name in selected_models:
model = MODEL_REGISTRY[model_name]
try:
if model["type"] == "text":
result = query_text_model(model["endpoint"], prompt)
elif model["type"] == "vision" and image is not None:
result = query_vision_model(model["endpoint"], prompt, image)
elif model["type"] == "audio" and audio is not None:
result = query_audio_model(model["endpoint"], audio)
else:
result = "Unsupported input for this model"
except Exception as e:
result = f"Error: {str(e)}"
outputs[model_name] = result
history.append((prompt, outputs))
return history, "", None, None
# UI
with gr.Blocks(theme=gr.themes.Soft(), title="Multimodal Model Comparison") as demo:
gr.Markdown("""
# Multimodal Chat + Model Comparison
Compare HuggingFace models across modalities
""")
with gr.Row():
with gr.Column(scale=3):
chatbot = gr.Chatbot(height=500)
prompt = gr.Textbox(
placeholder="Enter your prompt...",
label="Text Input"
)
with gr.Row():
image_input = gr.Image(type="pil", label="Image Input")
audio_input = gr.Audio(type="filepath", label="Audio Input")
submit = gr.Button("Send")
with gr.Column(scale=1):
gr.Markdown("### Model Selection")
model_selector = gr.CheckboxGroup(
choices=list(MODEL_REGISTRY.keys()),
value=["Text - Mistral 7B Instruct"],
label="Select Models"
)
clear = gr.Button("Clear")
state = gr.State([])
submit.click(
multimodal_chat,
inputs=[prompt, image_input, audio_input, model_selector, state],
outputs=[chatbot, prompt, image_input, audio_input]
)
clear.click(
lambda: [],
None,
chatbot
)
# Launch
if __name__ == "__main__":
demo.launch()
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