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import gradio as gr
import requests
import json
import os
# Retrieve the OpenRouter API Key from the Space secrets
API_KEY = os.getenv("OpenRouter_API_KEY")
# Define available models for selection
MODEL_OPTIONS = [
"openai/gpt-4o-mini-2024-07-18",
"meta-llama/llama-3.1-405b-instruct",
"nvidia/llama-3.1-nemotron-70b-instruct",
"qwen/qwen-2.5-7b-instruct",
"mistralai/mistral-large-2411",
"microsoft/phi-3-medium-128k-instruct",
"meta-llama/llama-3.1-405b-instruct:free",
"nousresearch/hermes-3-llama-3.1-405b:free",
"mistralai/mistral-7b-instruct:free",
"microsoft/phi-3-medium-128k-instruct:free",
"liquid/lfm-40b:free"
]
def generate_text(input_text, selected_model, history):
if history is None:
history = "" # Initialize history if it's None
response = requests.post(
url="https://openrouter.ai/api/v1/chat/completions",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
},
data=json.dumps({
"model": selected_model, # Use selected model
"messages": [{"role": "user", "content": input_text}],
"top_p": 1,
"temperature": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"repetition_penalty": 1,
"top_k": 0,
})
)
# Handle errors
if response.status_code != 200:
return f"Error: {response.status_code}, {response.text}", history
# Parse and return the content of the response
try:
response_json = response.json()
generated_response = response_json.get("choices", [{}])[0].get("message", {}).get("content", "No content returned.")
except json.JSONDecodeError:
generated_response = "Error: Unable to parse response."
# Append the new interaction to the history
history += f"User: {input_text}\nResponse: {generated_response}\n"
return generated_response, history
# Define the Gradio layout using Blocks
with gr.Blocks() as iface:
# Inject custom CSS using gr.HTML()
gr.HTML("""
<style>
#output-response, #output-history {
height: 300px;
overflow: auto;
border: 1px solid #ddd;
padding: 10px;
}
</style>
""")
input_text = gr.Textbox(lines=2, label="Input Text", placeholder="Enter your query here")
selected_model = gr.Dropdown(choices=MODEL_OPTIONS, label="Select Model", value=MODEL_OPTIONS[0])
output_response = gr.Textbox(label="Response", placeholder="Response will be shown here")
output_history = gr.Textbox(label="History", placeholder="Interaction history will be shown here", lines=10, interactive=False)
# Trigger the function when the user inputs text
generate_button = gr.Button("Generate")
generate_button.click(generate_text, inputs=[input_text, selected_model, gr.State()], outputs=[output_response, output_history])
# Launch the interface
iface.launch()
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