Spaces:
Running
Running
File size: 2,689 Bytes
66ff3e9 8658e51 b944409 991d86a 2a864c8 66ff3e9 991d86a 2a23908 c1a4bd7 991d86a c62ab32 2a864c8 c62ab32 991d86a 2a864c8 991d86a 2a864c8 991d86a 4394f5b c62ab32 2a864c8 c62ab32 2a864c8 f67b086 2a864c8 66ff3e9 2a864c8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
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"
]
# History storage
history = []
def generate_comparisons_with_history(input_text, selected_models):
global history
results = {}
for model in selected_models:
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": model, # Use the current model
"messages": [{"role": "user", "content": input_text}],
"top_p": 1,
"temperature": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"repetition_penalty": 1,
"top_k": 0,
})
)
# Parse the response
if response.status_code == 200:
try:
response_json = response.json()
results[model] = response_json.get("choices", [{}])[0].get("message", {}).get("content", "No content returned.")
except json.JSONDecodeError:
results[model] = "Error: Unable to parse response."
else:
results[model] = f"Error: {response.status_code}, {response.text}"
# Add input and results to history
history_entry = {
"input": input_text,
"selected_models": selected_models,
"outputs": results
}
history.append(history_entry)
return results, history
# Create Gradio interface with multiple model selection and history
iface = gr.Interface(
fn=generate_comparisons_with_history,
inputs=[
gr.Textbox(lines=2, label="Input Text", placeholder="Enter your query here"),
gr.CheckboxGroup(choices=MODEL_OPTIONS, label="Select Models", value=[MODEL_OPTIONS[0]])
],
outputs=[
gr.JSON(label="Model Comparisons"),
gr.JSON(label="History")
],
title="Compare Outputs and Maintain History"
)
iface.launch()
|