Create app.py

app.py

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+from langchain.document_loaders import PyPDFLoader, OnlinePDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Pinecone
+from sentence_transformers import SentenceTransformer
+from langchain.chains.question_answering import load_qa_chain
+import pinecone
+import os
+from langchain.llms import LlamaCpp
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from huggingface_hub import hf_hub_download
+from langchain.chains.question_answering import load_qa_chain
+from langchain import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain.llms import CTransformers
+import torch
+from langchain.chains import LLMChain
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+import gradio as gr
+import time
+from transformers import pipeline
+from gtts import gTTS
+
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"
+PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY', '55f52f29-11e3-4b87-a6ba-9a5494dfdb58')
+PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV', 'asia-southeast1-gcp-free')
+embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+# initialize pinecone
+pinecone.init(
+    api_key=PINECONE_API_KEY,  # find at app.pinecone.io
+    environment='gcp-starter'  # next to api key in console
+)
+index_name = "rpl-llama" # put in the name of your pinecone index here
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+model_name_or_path = "TheBloke/Llama-2-7b-Chat-GGUF"
+model_basename = "llama-2-7b-chat.Q4_0.gguf"
+model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename)
+n_gpu_layers = 40  # Change this value based on your model and your GPU VRAM pool.
+n_batch = 512  # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
+
+# Make sure the model path is correct for your system!
+llm = LlamaCpp(
+    model_path=model_path,
+    n_gpu_layers=n_gpu_layers,
+    n_batch=n_batch,
+    callback_manager=callback_manager,
+    verbose=True, # Verbose is required to pass to the callback manager
+)
+prompt_template="""
+Use the embeddings, summarize and generate the answers to user's questions. Don't repeat sentences.
+
+
+Context: {docs}
+Question: {query}
+
+Only return the helpful answer below and nothing else.
+Helpful answer:
+"""
+
+PROMPT=PromptTemplate(template=prompt_template, input_variables=["docs", "query"])
+llm_chain = LLMChain(prompt=PROMPT, llm=llm)
+
+# Initialize the chatbot model
+asr = pipeline("automatic-speech-recognition", model="openai/whisper-small")
+global bot_message
+# Create a Gradio interface
+with gr.Blocks() as demo:
+
+    chatbot = gr.Chatbot(avatar_images=("human.png", "bot.png"), value=[[None, "Welcome to the Indore-Ekk Number Superstore family! We're thrilled to have you on board. \n How can I assist you today?"]])
+    with gr.Row(label="Voice Input and Output"):
+        with gr.Column(variant="panel"):
+            audio_file = gr.Audio(label='Voice based Input',source="microphone",type="filepath",optional=True)
+        with gr.Column(variant="panel"):
+            play_audio = gr.Audio(label='Output Audio', autoplay=True)
+    audio_out = gr.Textbox(visible=False)
+
+    with gr.Row(label="Voice Input and Output"):
+        with gr.Column(label='Text Based Input', variant="panel"):
+            msg = gr.Textbox(placeholder="Ask me your doubts")
+        with gr.Column(variant="panel"):
+          with gr.Row():
+            clear = gr.Button("Clear the Chatbot Conversation")
+
+    def text_to_speech(text):
+      var = gTTS(text = text,lang = 'en')
+      var.save('eng.mp3')
+      return gr.Audio.update(value='eng.mp3')
+
+    def user(user_message, history):
+        global query
+        global fck
+        query = user_message
+        fck = model_response(query)
+        print(user_message,fck)
+        return '', history + [[user_message, None]],gr.Textbox.update(value=fck)
+
+    def model_response(query):
+        global a
+        #query = "What is the leave policy?"
+        docs=docsearch.similarity_search(query)
+        docs = docs[0].page_content+docs[1].page_content+docs[2].page_content
+        a = llm_chain.run({'docs':docs,'query':query})
+        return a
+
+    def bot(history):
+      global bot_message
+      bot_message = model_response(query)
+      history[-1][1] = ""
+      for character in fck:
+          history[-1][1] += character
+          time.sleep(0.05)
+          yield history
+
+
+    def speech_to_text(audio_file,history):
+      if audio_file == None:
+        return "", history + [[None, None]]
+      else:
+        global query
+        global fck
+        text = asr(audio_file)["text"]
+        query = text
+        fck = model_response(query)
+        print(text)
+        return None, history + [[text, None]],gr.Textbox.update(value=fck)
+        #return text
+
+    audio_file.stop_recording(speech_to_text, [audio_file,chatbot], [audio_file,chatbot,audio_out], queue=False, show_progress=False).then(bot, chatbot, chatbot)
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot,audio_out], queue=False).then(
+        bot, chatbot, chatbot
+    )
+
+    clear.click(lambda: None, None, chatbot, queue=False)
+    audio_out.change(text_to_speech,inputs=[audio_out], outputs=play_audio)
+
+demo.queue()
+demo.launch(debug=True)