Update app.py

app.py

@@ -1,257 +1,25 @@
-import plotly.graph_objs as go
-from sklearn.cluster import KMeans
-from sklearn.decomposition import PCA
-import plotly.express as px
-import numpy as np
-import os
-import pprint
-import codecs
-import chardet
-import gradio as gr
-from langchain.llms import HuggingFacePipeline, OpenAIChat
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate
-from langchain.chains.conversation.memory import ConversationBufferMemory
-from EdgeGPT import Chatbot
-import whisper
-from datetime import datetime
-import json
-import requests
-from langchain.chains.question_answering import load_qa_chain
-import langchain
-
-
-class ChatbotClass:
-    def __init__(self):
-        FOLDER_PATH = './data/eqe-manual'
-        QUERY = 'How do I charge my vehicle?'
-        K = 10
-        self.whisper_model = whisper.load_model(name='tiny')
-
-        self.embeddings = HuggingFaceEmbeddings()
-
-        self.index = FAISS.load_local(
-            folder_path=FOLDER_PATH, embeddings=self.embeddings
-        )
-
-        self.llm = OpenAIChat(temperature=0)
-
-        self.memory = ConversationBufferMemory(
-            memory_key="chat_history", input_key="human_input", return_messages=True
-        )
-
-        self.keyword_chain = self.init_keyword_chain()
-        self.context_chain = self.init_context_chain()
-        self.document_retrieval_chain = self.init_document_retrieval()
-        self.conversation_chain = self.init_conversation()
-
-    def format_history(self, memory):
-        history = memory.chat_memory.messages
-        if len(history) == 0:
-            return []
-
-        formatted_history = []
-
-        for h in history:
-            if isinstance(h, langchain.schema.HumanMessage):
-                user_response = h.content
-            elif isinstance(h, langchain.schema.AIMessage):
-                ai_response = h.content
-                formatted_history.append((user_response, ai_response))
-
-        return formatted_history
-
-    def init_document_retrieval(self):
-        retrieve_documents_template = """This function retrieves exerts from a Vehicle Owner's Manual. The function is useful for adding vehicle-specific context to answer questions. Based on a request, determine if vehicle specific information is needed. Respond with "Yes" or "No". If the answer is both, respond with "Yes":\nrequest: How do I change the tire?\nresponse: Yes\nrequest: Hello\nresponse: No\nrequest: I was in an accident. What should I do?\nresponse: Yes\nrequest: {request}\nresponse:"""
-
-        prompt_template = PromptTemplate(
-            input_variables=["request"],
-            template=retrieve_documents_template
-        )
-
-        document_retrieval_chain = LLMChain(
-            llm=self.llm, prompt=prompt_template, verbose=True
-        )
-
-        return document_retrieval_chain
-
-    def init_keyword_chain(self):
-        keyword_template = """You are a vehicle owner searching for content in your vehicle's owner manual. Your job is to come up with keywords to use when searching inside your manual, based on a question you have.
-        Question: {question}
-        Keywords:"""
-
-        prompt_template = PromptTemplate(
-            input_variables=["question"], template=keyword_template
-        )
-
-        keyword_chain = LLMChain(
-            llm=self.llm, prompt=prompt_template, verbose=True)
-
-        return keyword_chain
-
-    def init_context_chain(self):
-        context_template = """You are a friendly and helpful chatbot having a conversation with a human.
-        Given the following extracted parts of a long document and a question, create a final answer.
-
-        {context}
-
-        {chat_history}
-        Human: {human_input}
-        Chatbot:"""
-
-        context_prompt = PromptTemplate(
-            input_variables=["chat_history", "human_input", "context"],
-            template=context_template
-        )
-
-        self.memory = ConversationBufferMemory(
-            memory_key="chat_history", input_key="human_input", return_messages=True
-        )
-
-        context_chain = load_qa_chain(
-            self.llm, chain_type="stuff", memory=self.memory, prompt=context_prompt
-        )
-
-        return context_chain
-
-    def init_conversation(self):
-        template = """You are a chatbot having a conversation with a human.
-
-        {chat_history}
-        Human: {human_input}
-        Chatbot:"""
-
-        prompt = PromptTemplate(
-            input_variables=["chat_history", "human_input"],
-            template=template
-        )
-
-        conversation_chain = LLMChain(
-            llm=self.llm,
-            prompt=prompt,
-            verbose=True,
-            memory=self.memory,
-        )
-
-        return conversation_chain
-
-
-    def transcribe_audio(self, audio_file, model):
-        result = self.whisper_model.transcribe(audio_file)
-        return result['text']
-
-
-    def ask_question(self, query, k=4):
-        tool_usage = self.document_retrieval_chain.run(query)
-        print('\033[1;32m'  f'search manual?: {tool_usage}'  "\033[0m")
-
-        chat_history = self.format_history(self.memory)
-
-        if tool_usage == 'Yes':
-            keywords = self.keyword_chain.run(question=query)
-            print('\033[1;32m'  f'keywords:{keywords}'  "\033[0m")
-
-            context = self.index.similarity_search(query=keywords, k=k)
-
-            result = self.context_chain.run(
-                input_documents=context, human_input=query
-            )
-        else:
-            result = self.conversation_chain.run(query)
-
-        return [(query, result)], chat_history
-
-
-    def invoke_exh_api(self, bot_response, bot_name='Zippy', voice_name='Fiona', idle_url='https://ugc-idle.s3-us-west-2.amazonaws.com/4a6a607a466bdf6605bbd97ef146751b.mp4', animation_pipeline='high_quality', bearer_token='eyJhbGciOiJIUzUxMiJ9.eyJ1c2VybmFtZSI6IndlYiJ9.LSzIQx6h61l5FXs52s0qcY8WqauET6z9nnxgSzvoNBx8RYEKm8OpOohcK8wjuwteV4ZGug4NOjoGQoUZIKH84A'):
-        if len(bot_response) > 200:
-            print('Input is over 200 characters. Shorten the message')
-
-        url = 'https://api.exh.ai/animations/v1/generate_lipsync'
-        headers = {
-            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.46',
-            'authority': 'api.exh.ai',
-            'accept': '*/*',
-            'accept-encoding': 'gzip, deflate, br',
-            'accept-language': 'en-US,en;q=0.9',
-            'authorization': f'Bearer {bearer_token}',
-            'content-type': 'application/json',
-            'origin': 'https://admin.exh.ai',
-            'referer': 'https://admin.exh.ai/',
-            'sec-ch-ua': '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': '"Windows"',
-            'sec-fetch-dest': 'empty',
-            'sec-fetch-mode': 'cors',
-            'sec-fetch-site': 'same-site',
-        }
-
-        data = {
-            'bot_name': bot_name,
-            'bot_response': bot_response,
-            'voice_name': voice_name,
-            'idle_url': idle_url,
-            'animation_pipeline': animation_pipeline,
-        }
-
-        r = requests.post(url, headers=headers, data=json.dumps(data))
-
-        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S%f')
-        outfile = f'talking_head_{timestamp}.mp4'
-
-        with open(outfile, 'wb') as f:
-            f.write(r.content)
-
-        return outfile
-
-
-    def predict(self, input_data, state=[], k=4, input_type='audio'):
-        if input_type == 'audio':
-            txt = self.transcribe_audio(input_data[0], self.whisper_model)
-        else:
-            txt = input_data[1]
-        result, chat_history = self.ask_question(txt, k=k)
-        state.append(chat_history)
-        return result, state
-
-
-    def predict_wrapper(self, input_text=None, input_audio=None):
-        if input_audio is not None:
-            result, state = self.predict(
-                input_data=(input_audio,), input_type='audio')
-        else:
-            result, state = self.predict(
-                input_data=('', input_text), input_type='text')
-        
-        response = result[0][1][:195]
-        avatar = self.invoke_exh_api(response)
-
-        return result,avatar
-
-
-man_chatbot = ChatbotClass()
-
-iface = gr.Interface(
-    fn=man_chatbot.predict_wrapper,
-    inputs=[gr.inputs.Textbox(label="Text Input"),
-            gr.inputs.Audio(source="microphone", type='filepath')],
-    outputs=[gr.outputs.Textbox(label="Result"),
-             gr.outputs.Video().style(width=360, height=360, container=True)]
-)
-iface.launch()
-
-'''
-iface.launch()
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot()
-    state = gr.State([])
-    
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(
-            container=False)
-        k_slider = gr.Slider(minimum=1, maximum=10, default=4,label='k')
-        txt.submit(man_chatbot.predict, [txt, state,k_slider],[chatbot,state])
-
-demo.launch()
-'''
+from InnovationHub.llm.vector_store import *
+from InnovationHub.llm.chain import *
+
+
+def start_ui():
+    chatbot_interface = gradio.Interface(
+        fn=chat,
+        inputs=["text",
+                gradio.inputs.Dropdown(vehicle_options, label="Select a Mercedes-Benz Owner's Manual")
+                #gradio.inputs.Slider(minimum=1, maximum=10, step=1, label="k"),
+                #gradio.inputs.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Temperature")
+                ],
+        outputs="text",
+        title="Mercedes-Benz Owner's Manual",
+        description="Ask questions to get answers from the Mercedes-Benz Owner's manual. <br/><font size='-2'><u>Disclaimer:</u> THIS IS NOT OFFICIAL AND MAY NOT BE AVAILABLE ALL THE TIME. ALWAYS LOOK AT THE OFFICIAL DOCUMENTATION at https://www.mbusa.com/en/owners/manuals</font>",
+        examples=[["What are the different features of the dashboard console?", "2023 S-Class", 10, 0.01],
+                  ["What is flacon?", "2023 S-Class", 10, 0.01],
+                  ["What is hyperscreen?", "2023 EQS", 10, 0.01]],
+        article = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=kaushikdatta.owner-manual" alt="visitor badge"/></center>'
+    )
+
+    chatbot_interface.launch()
+
+if __name__ == '__main__':
+    start_ui()