Add feature chatgpt and bard

app.py

@@ -2,6 +2,9 @@ import gradio as gr
 from transformers import pipeline
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from openai_model import *
+from bard_model import *
+from llm_model import *
 import tensorflow
 import torch
 import random
@@ -13,42 +16,79 @@ global default_model_name
 default_model_name = "google/flan-t5-base"
 
 
-def predict(input_text, model_name):
-    if model_name == "":
-        model_name = default_model_name
+def predict(input_text, input_prompt, input_model1, input_model2, input_model3):
+    if input_model1!="":
+        model_name = input_model1
+        response_text = llm_predict(input_text, model_name)
+        return response_text
+    elif input_model2!="":
+        model_name = input_model2
+        response_text = openai_predict(input_text, input_prompt, model_name)
+        return response_text
+    else:
+        model_name = input_model3
+        response_text = bard_predict(input_text, model_name)
+        return response_text
 
-    pipe = pipeline("text2text-generation", model=model_name)
-    generated_text = pipe(input_text, max_new_tokens=1000)
 
-    return generated_text[0]['generated_text']
 
+def select_choice(choice):
+    if choice == "Custom Large Language Model (LLM)":
+        return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
+    elif choice == "OpenAI 'GPT-3.5-Turbo' Model":
+        return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)]
+    else:
+        return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
 
-with gr.Blocks() as demo:
+input_model1 = gr.Textbox(label="Enter LLM name:")
+input_model2 = gr.Textbox(label="Enter OpenAI API KEY:")
+input_model3 = gr.Textbox(label="Enter Google Bard API KEY:")
+
+
+with gr.Blocks(theme=gr.themes.Glass(primary_hue="blue", secondary_hue="sky", neutral_hue="stone")) as demo:
     gr.Markdown(
     """
     # Chatbot to interact with different Large Language Models (LLMs) 
-    [Here](https://huggingface.co/models?pipeline_tag=text2text-generation) are some popular text2text large lamguage models.  
-    Or use default model **"google/flan-t5-base"**
+    [Here](https://huggingface.co/models?pipeline_tag=text2text-generation) are some popular custom text2text large lamguage models.  
+    For example:  **"google/flan-t5-base"** 
+    OR Your **OPEN AI API KEY** to chat with **ChatGPT** 
+    OR Your **Google Bard API KEY** to chat with **Google Bard**
     """)
-    input_model = gr.Textbox(label="Enter a custom Large Language Model name (LLM):")
-    chatbot = gr.Chatbot(height=300, label="A chatbot to interact with llm", avatar_images=((os.path.join(os.path.dirname(__file__), "user.png")), (os.path.join(os.path.dirname(__file__), "bot.png"))))
-    user_input = gr.Textbox()
-    clear = gr.ClearButton([user_input, chatbot, input_model])
+    input_models = gr.Radio(choices=["Custom Large Language Model (LLM)", "OpenAI 'GPT-3.5-Turbo' Model", 
+    "Google Bard Model"], label="Please Select which model you want to use:", 
+    value="Custom Large Language Model (LLM):")
+
+    with gr.Row(visible=True) as row:
+        with gr.Column(visible=True) as col:
+            with gr.Row(visible=True) as rowA:        
+                input_model1.render()
+            with gr.Row(visible=False) as rowB:
+                input_model2.render()
+            with gr.Row(visible=False) as rowC:
+                input_model3.render()
+    
+   
+    chatbot = gr.Chatbot(height=300, label="A chatbot to interact with LLM", avatar_images=((os.path.join(os.path.dirname(__file__), "user.png")), (os.path.join(os.path.dirname(__file__), "bot.png"))))
+    user_input = gr.Textbox(label="Enter a message:")
+    clear = gr.ClearButton(value="Clear")
+    custom_prompt = gr.Textbox(label="Enter a custom prompt to use:", lines=15, max_lines=20, placeholder="In just 150 words summarize this text...")
+    
+    input_models.change(fn=select_choice, inputs=input_models, outputs=[rowA, rowB, rowC], queue=False) 
     
     def user(user_message, chat_history):
         return "", chat_history + [[user_message, None]]
 
-    def respond(chat_history, input_model):
-        bot_message = predict(chat_history[-1][0], input_model)
+    def respond(chat_history, custom_prompt, input_model1, input_model2, input_model3):
+        bot_message = predict(chat_history[-1][0], custom_prompt, input_model1, input_model2, input_model3)
         chat_history[-1][1] = bot_message
         time.sleep(2)
         return chat_history
     
     user_input.submit(user, [user_input, chatbot], [user_input, chatbot], queue=False).then(
-                    respond, [chatbot, input_model], chatbot
+                    respond, [chatbot, custom_prompt, input_model1, input_model2, input_model3], chatbot
     )
     
-    clear.click(lambda: None, None, chatbot, queue=False)
+    clear.click(lambda: [None, None, None, None, None, None], outputs=[user_input, chatbot, custom_prompt, input_model1, input_model2, input_model3], queue=False)
 
 demo.queue()
 demo.launch()

bard_model.py

@@ -0,0 +1,10 @@
+from bardapi import Bard
+
+
+
+def bard_predict(input_text, bard_api_key):
+
+    bard = Bard(api_key=bard_api_key)
+    response = bard.get_answer(input_text)
+    
+    return response['content']

llm_model.py

@@ -0,0 +1,9 @@
+from transformers import pipeline
+import tensorflow
+import torch
+
+
+def llm_predict(input_text, model_name):
+    pipe = pipeline("text2text-generation", model=model_name)
+    generated_text = pipe(input_text, max_new_tokens=1000)
+    return generated_text[0]['generated_text']

openai_model.py

@@ -0,0 +1,15 @@
+import openai
+import sys
+import os
+
+
+def openai_predict(message, input_prompt, openai_api_key):
+    openai.api_key = openai_api_key
+
+    response = openai.ChatCompletion.create(
+        model='gpt-3.5-turbo',
+        messages=[{"role": "user", "content": f"{input_prompt}: {message}"}],
+        temperature=1.0
+    )
+    
+    return response["choices"][0]["message"]["content"]

requirements.txt

@@ -1,4 +1,6 @@
+bardapi
 gradio
+openai
 tensorflow
 tensorflow_intel
 torch