Added support for palm

app.py

@@ -9,7 +9,7 @@ p = pipeline("automatic-speech-recognition")
 
 """Use text to call chat method from main.py"""
 
-models = ["GPT-3.5", "Flan UL2", "Flan T5"]
+models = ["GPT-3.5", "Flan UL2", "Flan T5", "Palm"]
 
 name = os.environ.get("name", "Rohan")
 
@@ -76,19 +76,21 @@ def upload_file(files, history, model):
     print("Ingesting files: " + str(file_paths))
     text = 'Uploaded a file'
     if ingest_files(file_paths, model):
-        response = 'Files are ingested'
+        response = 'Files are ingested. Roar now!'
     else:
-        response = 'Files are not ingested'
+        response = 'Files are not ingested. Please try again.'
 
     history = history + [(text, response)]
     return history
 
 theme = gr.Theme.from_hub("snehilsanyal/scikit-learn")
 
-theme.block_background_fill = gr.themes.colors.neutral.c200
+theme.block_background_fill = gr.themes.colors.neutral.c100
+theme.block_border_width = '2px'
+theme.block_border_radius = '10px'
 
 
-with gr.Blocks(theme) as demo:
+with gr.Blocks(theme=theme, title='Roar!') as demo:
     # Add image of Roar Logo from local directory
     gr.HTML('<img src="file/assets/logo.png" style="width: 100px; height: 100px; margin: 0 auto;border:5px solid orange;border-radius: 50%; display: block">')
     # Title on top in middle of the page
@@ -98,22 +100,23 @@ with gr.Blocks(theme) as demo:
 
     with gr.Row():
         # Create radio button to select model
-        radio = gr.Radio(models, label="Choose a model", value="GPT-3.5", type="value", visible=False)
+        radio = gr.Radio(models, label="Choose a model", value="GPT-3.5", type="value")
     with gr.Row():
         with gr.Column(scale=0.6):
             txt = gr.Textbox(
-                label="Rohan Bot",
+                label="Let's hear the roar!",
                 placeholder="Enter text and press enter, or upload a file", lines=1
             ).style(container=False)
 
         with gr.Column(scale=0.2):
-            upload = gr.UploadButton(label="Upload a file", type="file", file_count='multiple', file_types=['docx', 'txt', 'pdf', 'html']).style(container=False)
+            upload = gr.UploadButton(label="Roar on a file", type="file", file_count='multiple', file_types=['docx', 'txt', 'pdf', 'html']).style(container=False)
 
         with gr.Column(scale=0.2):
-            audio = gr.Audio(source="microphone", type="filepath").style(container=False)
+            audio = gr.Audio(source="microphone", type="filepath", label="Let me hear your roar!").style(container=False)
 
     with gr.Row():
-        gr.Examples(examples=['What are you an expert of?'], inputs=[txt], label="Examples")
+        gr.Examples(examples=['Roar it! What are you an expert of?', ' Roar it! What are you currently doing?',
+                              'Roar it! What is your opinion on Large Language Models?'], inputs=[txt], label="Examples")
 
     txt.submit(add_text, [chatbot, txt, radio], [chatbot, txt], postprocess=False).then(
         bot, chatbot, chatbot
@@ -133,4 +136,4 @@ with gr.Blocks(theme) as demo:
 if __name__ == "__main__":
     demo.queue()
     demo.queue(concurrency_count=5)
-    demo.launch(debug=True)
+    demo.launch(debug=True, favicon_path="file/assets/logo.png")

models/hf_vs.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:625e8ad0ba047ca6c1433b0f262471d94cbe5969a3ac5663df1e3bf60e47d32c
+size 49561

models/palm_vs.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a4fcb8df6e6711a1850b701dbb82ec9395e9c0e9574a0b0cc6afb632bfd3390
+size 49561

requirements.txt

@@ -8,4 +8,5 @@ gtts
 torch
 tiktoken
 huggingface-hub
-gradio
+gradio
+google-generativeai

utils.py

@@ -18,6 +18,7 @@ from langchain.prompts.chat import (
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.vectorstores.faiss import FAISS
 from langchain.cache import InMemoryCache
+from langchain.chat_models import ChatGooglePalm
 import traceback
 
 
@@ -25,7 +26,7 @@ langchain.llm_cache = InMemoryCache()
 
 global model_name
 
-models = ["GPT-3.5", "Flan UL2", "GPT-4", "Flan T5"]
+models = ["GPT-3.5", "Flan UL2", "GPT-4", "Flan T5", "Palm"]
 
 pickle_file = "_vs.pkl"
 updated_pickle_file = "_vs_updated.pkl"
@@ -89,6 +90,8 @@ def set_model(model):
     elif model == "Flan T5":
         print("Loading Flan T5")
         llm = HuggingFaceHub(repo_id="google/flan-t5-base", model_kwargs={"temperature": 0.1})
+    elif model == "Palm":
+        llm = ChatGooglePalm(temperature=0)
     else:
         print("Loading GPT-3.5 from else")
         llm = OpenAI(model_name="text-davinci-002", temperature=0.1)
@@ -146,6 +149,8 @@ def get_file_path(model, file):
     # If model is GPT3.5 or GPT4 return models_folder + openai + file else return models_folder + hf + file
     if model == "GPT-3.5" or model == "GPT-4":
         return models_folder + "openai" + file
+    elif model == "Palm":
+        return models_folder + "palm" + file
     else:
         return models_folder + "hf" + file