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import base64
import requests
from io import BytesIO
from PIL import Image
import gradio as gr
def encode_image(img):
"""
Encodes a PIL Image to a base64 string in PNG format.
"""
buffered = BytesIO()
img.save(buffered, format="PNG")
encoded_string = base64.b64encode(buffered.getvalue()).decode("utf-8")
return encoded_string
def get_api_response(api_key, user_inputs):
"""
Sends the user message and image to the Hyperbolic API and retrieves the response.
"""
if not api_key:
return {"error": "API key is required."}
if not user_inputs.get("text") and not user_inputs.get("image"):
return {"error": "Please provide a text message, an image, or both."}
try:
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}",
}
messages = []
if user_inputs.get("text"):
messages.append({
"type": "text",
"text": user_inputs["text"]
})
if user_inputs.get("image"):
# Open the uploaded image (already a PIL Image)
base64_img = encode_image(user_inputs["image"])
messages.append({
"type": "image_url",
"image_url": {"url": f"data:image/png;base64,{base64_img}"}
})
payload = {
"messages": [
{
"role": "user",
"content": messages,
}
],
"model": "Qwen/Qwen2-VL-72B-Instruct",
"max_tokens": 2048,
"temperature": 0.7,
"top_p": 0.9,
}
api_endpoint = "https://api.hyperbolic.xyz/v1/chat/completions"
response = requests.post(api_endpoint, headers=headers, json=payload)
# Check if the request was successful
if response.status_code == 200:
api_response = response.json()
# Extract the AI's reply (assuming the response structure)
ai_reply = api_response.get("choices", [{}])[0].get("message", {}).get("content", "No response content.")
return {"response": ai_reply}
else:
return {"error": f"API Error: {response.status_code} - {response.text}"}
except Exception as e:
return {"error": str(e)}
def chatbot_response(api_key, user_inputs, history):
"""
Handles the chatbot interaction by updating the conversation history.
"""
user_text = user_inputs.get("text")
user_image = user_inputs.get("image")
# Append the user's message to the history
if user_text or user_image:
history.append(("User", user_text, user_image))
# Get the API response
api_result = get_api_response(api_key, user_inputs)
if "error" in api_result:
ai_message = f"Error: {api_result['error']}"
else:
ai_message = api_result["response"]
# Append the AI's response to the history
history.append(("AI", ai_message, None))
return history, history
# Define the Gradio interface
with gr.Blocks() as demo:
gr.Markdown(
"""
# 🖼️ Qwen2-VL-72B-Instruct with Hyperbolic API
Engage in a conversation with the AI by sending text messages and/or uploading images. Enter your Hyperbolic API key to get started.
"""
)
with gr.Row():
api_key_input = gr.Textbox(
label="🔑 Hyperbolic API Key",
type="password",
placeholder="Enter your API key here",
interactive=True
)
chatbot = gr.Chatbot(label="💬 Chatbot") # Removed `.style()` method
with gr.Row():
chat_input = gr.MultimodalTextbox(
label="Your Input",
placeholder="Type your message and/or upload an image...",
file_count="multiple", # Allows multiple files if needed
interactive=True
)
send_button = gr.Button("📤 Send")
# Hidden state to keep track of the conversation history
state = gr.State([])
send_button.click(
fn=chatbot_response,
inputs=[api_key_input, chat_input, state],
outputs=[chatbot, state]
)
gr.Markdown(
"""
---
**Note:** Your API key is used only for this session and is not stored. Ensure you trust the environment in which you're running this application.
"""
)
# Launch the Gradio app
if __name__ == "__main__":
demo.launch()
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