Added API key validation, get a working version of the app

app.py

@@ -1,16 +1,58 @@
 import gradio as gr
 import os
 import requests
+from huggingface_hub import InferenceClient
 import google.generativeai as genai
 import openai
 
+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-4-0613",
-      messages=[{"role": "system", "content": "Suppose that you are a talented diagnostician"},
+      messages=[{"role": "system", "content": "You are a talented diagnostician who is diagnosing a patient."},
                 {"role": "user", "content": prompt}],
       temperature=temperature,
       max_tokens=50,
@@ -35,32 +77,33 @@ def generate_text_gemini(key, prompt, temperature, top_p):
 
 
 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
+    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(key, model, top_k, temperature, symptom_prompt):
 
+    model_map = {
+        "GPT-3.5-Turbo": "GPT",
+        "Llama-3": "Llama",
+        "Gemini-1.5": "Gemini"
+    }
     if symptom_prompt:
         if "GPT" in model:
             message = generate_text_chatgpt(key, symptom_prompt, temperature, top_k)
@@ -75,6 +118,27 @@ def diagnose(key, model, top_k, temperature, symptom_prompt):
     
     return message
 
+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, api_key, model):
+    if symptoms_text.strip() and validate_api_key(api_key, model):
+        return gr.update(interactive=True)
+    return gr.update(interactive=False)
 
 
 with gr.Blocks() as ui:
@@ -82,22 +146,29 @@ 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")
+                             choices=["GPT-3.5-Turbo", "Llama-3", "Gemini-1.5"])
+            is_valid = False
+            key = gr.Textbox(label="Please input the API key for your GPT model", type="password")
+            status_message = gr.HTML(label="Validation Status")
+            key.input(fn=api_check_msg, inputs=[key, model], outputs=status_message)
+            button = gr.Button(value="Don't have an API key? Get one for the GPT model here.", link="https://platform.openai.com/account/api-keys")
+            model.change(update_model_components, inputs=model, outputs=[key, button])
+            # 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")
+            gr.Markdown("## Hello, Welcome to the GUI by Team #9.")
+            temperature = gr.Slider(0.0, 1.0, value=0.7, step = 0.05, 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")
+            llm_btn = gr.Button(value="Diagnose Disease", variant="primary", elem_id="diagnose", interactive=False)
+            symptoms.input(toggle_button, inputs=[symptoms, key, model], outputs=llm_btn)
+            key.input(toggle_button, inputs=[symptoms, key, model], outputs=llm_btn)
+            model.change(toggle_button, inputs=[symptoms, key, model], outputs=llm_btn)
             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")
+            llm_btn.click(fn=diagnose, inputs=[key, model, top_p, temperature, symptoms], outputs=output, api_name="auditor")
 
 
 ui.launch(share=True)