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import gradio as gr
import requests
import google.generativeai as genai
import openai
from collections import Counter
from huggingface_hub import InferenceClient
def api_check_msg(api_key, selected_model):
res = validate_api_key(api_key, selected_model)
return res["message"]
def validate_api_key(api_key, selected_model):
# Check if the API key is valid for GPT-3.5-Turbo
if "GPT" in selected_model:
url = "https://api.openai.com/v1/models"
headers = {
"Authorization": f"Bearer {api_key}"
}
try:
response = requests.get(url, headers=headers)
if response.status_code == 200:
return {"is_valid": True, "message": '<p style="color: green;">API Key is valid!</p>'}
else:
return {"is_valid": False, "message": f'<p style="color: red;">Invalid OpenAI API Key. Status code: {response.status_code}</p>'}
except requests.exceptions.RequestException as e:
return {"is_valid": False, "message": f'<p style="color: red;">Invalid OpenAI API Key. Error: {e}</p>'}
elif "Llama" in selected_model:
url = "https://huggingface.co/api/whoami-v2"
headers = {
"Authorization": f"Bearer {api_key}"
}
try:
response = requests.get(url, headers=headers)
if response.status_code == 200:
return {"is_valid": True, "message": '<p style="color: green;">API Key is valid!</p>'}
else:
return {"is_valid": False, "message": f'<p style="color: red;">Invalid Hugging Face API Key. Status code: {response.status_code}</p>'}
except requests.exceptions.RequestException as e:
return {"is_valid": False, "message": f'<p style="color: red;">Invalid Hugging Face API Key. Error: {e}</p>'}
elif "Gemini" in selected_model:
try:
genai.configure(api_key=api_key)
model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content("Help me diagnose the patient.")
return {"is_valid": True, "message": '<p style="color: green;">API Key is valid!</p>'}
except Exception as e:
return {"is_valid": False, "message": f'<p style="color: red;">Invalid Google API Key. Error: {e}</p>'}
def generate_text_chatgpt(key, prompt, temperature, top_p):
openai.api_key = key
response = openai.chat.completions.create(
model="gpt-3.5-turbo-1106",
messages=[{"role": "system", "content": "You are a talented diagnostician who is diagnosing a patient."},
{"role": "user", "content": prompt}],
temperature=temperature,
max_tokens=50,
top_p=top_p,
frequency_penalty=0
)
return response.choices[0].message.content
def generate_text_gemini(key, prompt, temperature, top_p):
genai.configure(api_key=key)
generation_config = genai.GenerationConfig(
max_output_tokens=len(prompt)+50,
temperature=temperature,
top_p=top_p,
)
model = genai.GenerativeModel("gemini-1.5-flash", generation_config=generation_config)
response = model.generate_content(prompt)
return response.text
def generate_text_llama(key, prompt, temperature, top_p):
model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
client = InferenceClient(api_key=key)
messages = [{"role": "system", "content": "You are a talented diagnostician who is diagnosing a patient."},
{"role": "user","content": prompt}]
completion = client.chat.completions.create(
model=model_name,
messages=messages,
max_tokens=len(prompt)+50,
temperature=temperature,
top_p=top_p
)
response = completion.choices[0].message.content
if len(response) > len(prompt):
return response[len(prompt):]
return response
def diagnose(gpt_key, llama_key, gemini_key, top_p, temperature, symptoms):
if symptoms:
gpt_message = generate_text_chatgpt(gpt_key, symptoms, temperature, top_p)
llama_message = generate_text_llama(llama_key, symptoms, temperature, top_p)
gemini_message = generate_text_gemini(gemini_key, symptoms, temperature, top_p)
outputs = [gpt_message, llama_message, gemini_message]
output_counts = Counter(outputs)
majority_output, majority_count = output_counts.most_common(1)[0]
confidence = (majority_count / len(outputs)) * 100
else:
majority_output = "Please add the symptoms data to start the ranking process"
confidence = 0
return majority_output, confidence
def update_model_components(selected_model):
model_map = {
"GPT-3.5-Turbo": "GPT",
"Llama-3": "Llama",
"Gemini-1.5": "Gemini"
}
link_map = {
"GPT-3.5-Turbo": "https://platform.openai.com/account/api-keys",
"Llama-3": "https://hf.co/settings/tokens",
"Gemini-1.5": "https://aistudio.google.com/apikey"
}
textbox_label = f"Please input the API key for your {model_map[selected_model]} model"
button_value = f"Don't have an API key? Get one for the {model_map[selected_model]} model here."
button_link = link_map[selected_model]
return gr.update(label=textbox_label), gr.update(value=button_value, link=button_link)
def toggle_button(symptoms_text, gpt_key, llama_key, gemini_key):
if symptoms_text.strip() and validate_api_key(gpt_key, "GPT") and \
validate_api_key(llama_key, "Llama") and validate_api_key(gemini_key, "Gemini"):
return gr.update(interactive=True)
return gr.update(interactive=False)
with gr.Blocks() as ui:
with gr.Row(equal_height=500):
with gr.Column(scale=1, min_width=300):
gpt_key = gr.Textbox(label="Please input your GPT key", type="password")
llama_key = gr.Textbox(label="Please input your Llama key", type="password")
gemini_key = gr.Textbox(label="Please input your Gemini key", type="password")
is_valid = False
status_message = gr.HTML(label="Validation Status")
gpt_key.input(fn=api_check_msg, inputs=[gpt_key, gr.Textbox(value="GPT", visible=False)], outputs=status_message)
status_message = gr.HTML(label="Validation Status")
llama_key.input(fn=api_check_msg, inputs=[llama_key, gr.Textbox(value="Llama", visible=False)], outputs=status_message)
status_message = gr.HTML(label="Validation Status")
gemini_key.input(fn=api_check_msg, inputs=[gemini_key, gr.Textbox(value="Gemini", visible=False)], outputs=status_message)
gr.Button(value="Don't have an LLM key? Get one through the below links.")
gr.Button(value="OpenAi Key", link="https://platform.openai.com/account/api-keys")
gr.Button(value="Meta Llama Key", link="https://platform.openai.com/account/api-keys")
gr.Button(value="Gemini Key", link="https://platform.openai.com/account/api-keys")
gr.ClearButton(gpt_key, llama_key, gemini_key, variant="primary")
with gr.Column(scale=2, min_width=600):
gr.Markdown("### Hello, Welcome to the GUI by Team #9.")
temperature = gr.Slider(0.0, 1.0, value=0.7, step = 0.01, label="Temperature", info="Set the Temperature")
top_p = gr.Slider(1, 10, value=3, step = 1, label="top-p value", info="Set the sampling nucleus parameter")
symptoms = gr.Textbox(label="Add the symptom data in the input to receive diagnosis")
llm_btn = gr.Button(value="Diagnose Disease", variant="primary", elem_id="diagnose", interactive=False)
symptoms.input(toggle_button, inputs=[symptoms, gpt_key, llama_key, gemini_key], outputs=llm_btn)
output = gr.Textbox(label="LLM output with majority vote and confidence", interactive=False, placeholder="Output will appear here...")
llm_btn.click(fn=diagnose, inputs=[gpt_key, llama_key, gemini_key, top_p, temperature, symptoms], outputs=output, api_name="LLM_Comparator")
ui.launch(share=True) |