init commit

app.py

@@ -0,0 +1,106 @@
+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:
+    message = "Hello, Welcome to the GUI by Team #9."
+
+    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, message, variant="primary")
+            
+        with gr.Column(scale=2, min_width=600):
+            message = gr.Textbox(label="", value=message, interactive=False, visible=True)
+            output = gr.Textbox(label="Model output status", value="Model hasn't run yet.")
+            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")
+            llm_btn.click(fn=diagnose, inputs=[key, model, top_k, temperature, symptoms], outputs=output, api_name="auditor")
+            output = gr.Textbox(label="LLM output status", value=output.value)
+
+
+
+ui.launch(share=True)