app.py

import gradio as gr
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import torch

import os
import sys
sys.path.append('../..')

#langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from langchain.schema.runnable import Runnable
from langchain.schema.runnable.config import RunnableConfig
from langchain.chains import (
    LLMChain, ConversationalRetrievalChain)
from langchain.vectorstores import Chroma
from langchain.memory import ConversationBufferMemory
from langchain.chains import LLMChain
from langchain.prompts.prompt import PromptTemplate
from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate
from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate,  MessagesPlaceholder
from langchain.document_loaders import PyPDFDirectoryLoader

from langchain_community.llms import HuggingFaceHub

from pydantic import BaseModel
import shutil

# Cell 1: Image Classification Model
image_pipeline = pipeline(task="image-classification", model="microsoft/resnet-50")

def predict_image(input_img):
    predictions = image_pipeline(input_img)
    return input_img, {p["label"]: p["score"] for p in predictions} 

image_gradio_app = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
    title="Hot Hot Dog? Or Not?",
)

# Cell 2: Chatbot Model

loader = PyPDFDirectoryLoader('pdfs')
data=loader.load()
# split documents
text_splitter = RecursiveCharacterTextSplitter(
    separator="\n",
    chunk_size=1000,
    chunk_overlap=150,
    length_function=len
)
docs = text_splitter.split_documents(data)
# define embedding
embeddings = HuggingFaceEmbeddings(model_name='thenlper/gte-small')
# create vector database from data
persist_directory = 'docs/chroma/'

# Remove old database files if any
shutil.rmtree(persist_directory, ignore_errors=True)
vectordb = Chroma.from_documents(
    documents=docs,
    embedding=embeddings,
    persist_directory=persist_directory
)
# define retriever
retriever = vectordb.as_retriever(search_type="mmr")
template = """
Your name is AngryGreta and you are a recycling chatbot with the objective to anwer questions from user in English or Spanish /
Use the following pieces of context to answer the question if the question is related with recycling /
No more than two chunks of context /
Answer in the same language of the question /
Always say "thanks for asking!" at the end of the answer. 
context: {context}
chat history: {chat_history}
question: {question}
"""

# Create the chat prompt templates
system_prompt = SystemMessagePromptTemplate.from_template(template)
qa_prompt = ChatPromptTemplate(
	messages=[
		system_prompt, 
		MessagesPlaceholder(variable_name="chat_history"), 
		HumanMessagePromptTemplate.from_template("{question}")
    ]
)
llm = HuggingFaceHub(
    repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
    task="text-generation",
    model_kwargs={
        "max_new_tokens": 512,
        "top_k": 30,
        "temperature": 0.1,
        "repetition_penalty": 1.03,
    },
)
llm_chain = LLMChain(llm=llm, prompt=qa_prompt)

memory = ConversationBufferMemory(llm=llm, memory_key="chat_history", output_key='answer', return_messages=False)

qa_chain = ConversationalRetrievalChain.from_llm(
    llm = llm,
    memory = memory,
    retriever = retriever,
    verbose = True,
    combine_docs_chain_kwargs={'prompt': qa_prompt},
    get_chat_history = lambda h : h
)

def chat_interface(question):

    answer = qa_chain.run({"question": question})
    print("Debug: answer from qa_chain.run:", answer)

    # Check the structure of the result
    if isinstance(answer, str):
        return answer  # If the result is a string, return it directly
    else:
        return "Unexpected answer format"

chatbot_gradio_app = gr.Interface(
    fn=chat_interface, 
    inputs=[
        gr.inputs.Textbox(lines=1, label="Question"),
    ], 
    outputs="text"
)

# Combine both interfaces into a single app
app=gr.TabbedInterface(
    [image_gradio_app, chatbot_gradio_app],
    tab_names=["image","chatbot"]
)

app.queue()
app.launch()