Spaces:
Running
Running
| import gradio as gr | |
| import os | |
| import requests | |
| import google.generativeai as genai | |
| import openai | |
| def generate_text_chatgpt(key, prompt, temperature, top_p): | |
| openai.api_key = key | |
| response = openai.chat.completions.create( | |
| model="gpt-4-0613", | |
| messages=[{"role": "system", "content": "Suppose that you are a talented diagnostician"}, | |
| {"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/Llama-3.1-8B-Instruct" | |
| API_URL = f"https://api-inference.huggingface.co/models/{model_name}" | |
| headers = {"Authorization": f"Bearer {key}"} | |
| payload = { | |
| "inputs": prompt, | |
| "parameters": { | |
| "temperature": temperature, | |
| "max_new_tokens": 50, | |
| "top_p": top_p, | |
| } | |
| } | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| resp_obj = response.json() | |
| if isinstance(resp_obj, list): | |
| resp = resp_obj[0] | |
| if 'generated_text' in resp: | |
| if len(resp['generated_text']) > len(prompt): | |
| return resp['generated_text'][len(prompt):] | |
| return resp['generated_text'] | |
| return resp | |
| return resp_obj | |
| def diagnose(key, model, top_k, temperature, symptom_prompt): | |
| if symptom_prompt: | |
| if "GPT" in model: | |
| message = generate_text_chatgpt(key, symptom_prompt, temperature, top_k) | |
| elif "Llama" in model: | |
| message = generate_text_llama(key, symptom_prompt, temperature, top_k) | |
| elif "Gemini" in model: | |
| message = generate_text_gemini(key, symptom_prompt, temperature, top_k) | |
| else: | |
| message = "Incorrect model, please try again." | |
| else: | |
| message = "Please add the symptoms data" | |
| return message | |
| with gr.Blocks() as ui: | |
| with gr.Row(equal_height=500): | |
| with gr.Column(scale=1, min_width=300): | |
| model = gr.Radio(label="LLM Selection", value="GPT-3.5-Turbo", | |
| choices=["GPT-3.5-Turbo", "Llama-3.1", "Gemini-1.5"]) | |
| key = gr.Textbox(label="Please input your LLM key", type="password") | |
| 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(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_k = gr.Slider(1, 10, value=3, step = 1, label="top-k value", info="Set the 'k' for top-k LLM responses") | |
| 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") | |
| output = gr.Textbox(label="LLM Output Status", interactive=False, placeholder="Output will appear here...") | |
| llm_btn.click(fn=diagnose, inputs=[key, model, top_k, temperature, symptoms], outputs=output, api_name="auditor") | |
| ui.launch(share=True) |