import gradio as gr import requests import google.generativeai as genai import openai from collections import Counter from huggingface_hub import InferenceClient import re 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": '

GPT API Key is valid!

'} else: return {"is_valid": False, "message": f'

Invalid OpenAI API Key. Status code: {response.status_code}

'} except requests.exceptions.RequestException as e: return {"is_valid": False, "message": f'

Invalid OpenAI API Key. Error: {e}

'} 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": '

Llama API Key is valid!

'} else: return {"is_valid": False, "message": f'

Invalid Hugging Face API Key. Status code: {response.status_code}

'} except requests.exceptions.RequestException as e: return {"is_valid": False, "message": f'

Invalid Hugging Face API Key. Error: {e}

'} 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": '

Gemini API Key is valid!

'} except Exception as e: return {"is_valid": False, "message": f'

Invalid Google API Key. Error: {e}

'} 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 based on the symptoms they provided."}, {"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 sanitize_outputs(outputs): sanitized_results = [] for output in outputs: output = output.replace("\n", " ") output = re.sub(r"(Diagnose:|Answer:)", "", output, flags=re.IGNORECASE).strip() diagnoses = ["Psoriasis", "Arthritis", "Bronchial Asthma", "Cervical spondylosis"] found_diagnoses = [disease for disease in diagnoses if disease in output] if found_diagnoses: sanitized_results.append(found_diagnoses[0]) else: sanitized_results.append("Unknown") # Handle case where no valid diagnosis is found return sanitized_results def diagnose(gpt_key, llama_key, gemini_key, top_p, temperature, symptoms): if symptoms: prompt = "Given the next set of symptoms, classify the diagnosis as one of the following: " prompt += "Psoriasis, Arthritis, Bronchial Asthma, Cervical spondylosis. Please only output the classified diagnosis and nothing after that." prompt += "Choose only one among the words Psoriasis, Arthritis, Bronchial Asthma or Cervical spondylosis" prompt += "Do not list the symptoms again in the response. Do not add any additional text. Do not attempt to explain your answer." prompt += symptoms prompt += "Your Diagnosis: []" gpt_message = generate_text_chatgpt(gpt_key, prompt, temperature, top_p) llama_message = generate_text_llama(llama_key, prompt, temperature, top_p) gemini_message = generate_text_gemini(gemini_key, prompt, temperature, top_p) outputs = [gpt_message, llama_message, gemini_message] outputs = sanitize_outputs(outputs) output_counts = Counter(outputs) majority_output, majority_count = output_counts.most_common(1)[0] confidence = int((majority_count / len(outputs)) * 100) return gpt_message, llama_message, gemini_message, majority_output, confidence else: return {"is_valid": False, "message": f'

Please add the symptoms data to start the ranking process.

'} 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.Markdown("### 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. This is the ranking API.") temperature = gr.Slider(0.0, 1.0, value=0.7, step = 0.01, label="Temperature", info="Set the Temperature") top_p = gr.Slider(0.0, 1.0, value=0.9, step = 0.05, 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) with gr.Row(equal_height=200): with gr.Column(scale=1, min_width=150): majority_output = gr.Textbox(label="Majority Output", interactive=False, placeholder="Majority Output") with gr.Column(scale=1, min_width=150): confidence = gr.Textbox(label="Confidence Score (%)", interactive=False, placeholder="Confidence Score") with gr.Row(equal_height=200): with gr.Column(scale=1, min_width=66): gpt_message = gr.Textbox(label="GPT Output", interactive=False, placeholder="GPT Output") with gr.Column(scale=1, min_width=66): llama_message = gr.Textbox(label="LLaMA Output", interactive=False, placeholder="LLaMA Output") with gr.Column(scale=1, min_width=66): gemini_message = gr.Textbox(label="Gemini Output", interactive=False, placeholder="Gemini Output") llm_btn.click(fn=diagnose, inputs=[gpt_key, llama_key, gemini_key, top_p, temperature, symptoms], outputs=[gpt_message, llama_message, gemini_message, majority_output, confidence], api_name="LLM_Comparator") ui.launch(share=True)