base/main.py

import os
import numpy as np
import warnings
import librosa
import streamlit as st
import tempfile
import json
from PIL import Image
import pandas as pd
from joblib import dump, load
import wikipedia
import requests
# import wikipediaapi
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.models import load_model
# import soundfile as sf
from audio_analysis import audio_signals
from audio_processing import extract_features
import os
from dotenv import load_dotenv
import json
import streamlit as st
from huggingface_hub import InferenceApi, login, InferenceClient


st.set_page_config(
    page_title="BirdSense",
    page_icon=":bird:",
    initial_sidebar_state="expanded",
    menu_items={
        'Get Help': 'https://ashok2216-myportfolio-github-io.vercel.app/#contact',
        'Report a bug': "https://ashok2216-myportfolio-github-io.vercel.app/#contact",
        'About': "https://ashok2216-myportfolio-github-io.vercel.app/"
    }
)

# Get the Hugging Face token from environment variables
load_dotenv()
hf_token = os.getenv("HF_TOKEN")
if hf_token is None:
    raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.")
# Authenticate with Hugging Face
login(hf_token)

# Model information and links
model_links = {
    "Zephyr-7B": "HuggingFaceH4/zephyr-7b-beta"
}
model_info = {
    "Zephyr-7B": {
        'description': """Zephyr 7B is a Huggingface model, fine-tuned for helpful and instructive interactions.""",
        'logo': 'https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png'
    }
}

# Inference API Initialization
client = InferenceClient('HuggingFaceH4/zephyr-7b-beta')

# Reset conversation button
def reset_conversation():
    return [
        {"role": "system", "content": "You are a knowledgeable and empathetic ornithologist assistant providing accurate and relevant information based on user input."}
    ]

# Initialize conversation and chat history
messages = reset_conversation()

# Display chat history
for message in messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

def respond(message, history, max_tokens, temperature, top_p):
    # Prepare the list of messages for the chat completion
    messages = [{"role": "system", "content": history[0]["content"]}]

    for val in history:
        if val["role"] == "user":
            messages.append({"role": "user", "content": val["content"]})
        elif val["role"] == "assistant":
            messages.append({"role": "assistant", "content": val["content"]})

    messages.append({"role": "user", "content": message})

    # Generate response
    response = ""
    response_container = st.empty()  # Placeholder to update the response text dynamically

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        # response_container.text(response)  # Stream the response

    return response
# Load environment variables
load_dotenv()
hf_token = os.getenv("HF_TOKEN")
if hf_token is None:
    raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.")
# Authenticate with Hugging Face
login(hf_token)

image = Image.open('logo.PNG')
st.image(
    image, width=250
)
st.subheader('Bird Species Classification')
# st.markdown('Sound of 114 Bird Species :bird: :penguin: :hatched_chick:')
st.header('', divider='rainbow')

@st.cache_data
def loaded_model(model_path):
    return load_model(model_path)

@st.cache_data
def predict_class(audio_path, model):
    extracted_feature = extract_features(audio_path)
    extracted_feature = extracted_feature.reshape(1, 1, extracted_feature.shape[0])
    prediction = model.predict(extracted_feature)
    predicted_class_index = np.argmax(prediction)
    print('HI',predicted_class_index)
    # predicted_class_label = label_encoder.inverse_transform([predicted_class_index])[0]
    return predicted_class_index


audio_file = st.file_uploader("Upload an Audio file", type=["mp3", "wav", "ogg"], accept_multiple_files=False)
# Load the model
model_path = 'bird_audio_classification_model.h5'
model = loaded_model(model_path)

class_file = open('classes.json', 'r').read()
labels_list = json.loads(class_file)

st.markdown('Download the Sample Audio here :point_down:')
st.page_link("https://dibird.com/", label="DiBird.com", icon="🐦")
st.subheader('Scientific Name of 114 Birds Species :bird:')

with st.container(height=300):
    st.markdown(list(labels_list.values()))
# birds = pd.DataFrame(class_file)
# st.table(birds)
st.header('', divider='rainbow')

if audio_file is not None:
    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
        tmp_file.write(audio_file.read())
        st.success("Audio file successfully uploaded and stored temporally.")
    file_path = tmp_file.name
    audio_data, sampling_rate = librosa.load(file_path)
    st.audio(audio_data, sample_rate=sampling_rate)
    audio_signals(file_path)
    # Predict the class
    y_predict = predict_class(file_path, model)
    # Display predicted class
    if str(y_predict) in labels_list.keys():
        pred = labels_list[str(y_predict)][:-6]
        st.subheader(f'Predicted Class: :rainbow[{pred}]') 
        st.image(wikipedia.page(pred).images[0], caption=labels_list[str(y_predict)][:-6], width=200)
        st.markdown(wikipedia.summary(pred))
        
        user_input = f"Explain about {pred} bird"
        # Generate and display assistant response   
        if user_input:
            response = respond(user_input, messages, max_tokens = 500, temperature = 0.70, top_p = 0.95)
            st.markdown(response)
            messages.append({"role": "assistant", "content": response})
            st.page_link(wikipedia.page(pred).url, label="Explore more in Wikipedia.com", icon="🌎")
            # st.session_state.messages.append({"role": "assistant", "content": response})
    else:
        st.write('Class not Found')      
else:
    st.markdown('File not Found!')