Upload 20 files

.devcontainer/devcontainer.json

@@ -0,0 +1,33 @@
+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "app.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run app.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+BirdSpecies_Classification.ipynb filter=lfs diff=lfs merge=lfs -text

.github/workflows/python-app.yml

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+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python application in Streamlit
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: "3.10"
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    # - name: Run Streamlit app
+    #   run: streamlit run app.py
+    - name: Deploy
+      # Only run this step if the branch is main
+      if: github.ref == 'refs/heads/main'
+      env:
+        deploy_url: ${{ secrets.RENDER_DEPLOY_HOOK_URL }}
+      run: |
+        curl "$deploy_url"

BirdSpecies_Classification.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3ba76fa455187b5856a3d5315ffc815b3e27feedc3f7c23761d17b71945ad23
+size 22378841

app.py

@@ -0,0 +1,195 @@
+'''Copyright 2024 Ashok Kumar
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.'''
+
+
+
+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"]):
+#         print(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())
+        
+    file_path = tmp_file.name
+    audio_data, sampling_rate = librosa.load(file_path)
+    st.audio(audio_data, sample_rate=sampling_rate)
+    st.success("Audio file successfully uploaded and stored temporally.")
+    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!')

audio_analysis.py

@@ -0,0 +1,94 @@
+'''Copyright 2024 Ashok Kumar
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.'''
+
+
+import os
+import matplotlib.pyplot as plt
+plt.style.use('dark_background')
+import seaborn as sns
+import numpy as np
+import librosa
+from IPython.display import Audio
+# import soundfile as sf
+import streamlit as st
+
+
+def audio_waveframe(file_path):
+    # Load the audio file
+    audio_data, sampling_rate = librosa.load(file_path)
+    # Calculate the duration of the audio file
+    duration = len(audio_data) / sampling_rate
+    # Create a time array for plotting
+    time = np.arange(0, duration, 1/sampling_rate)
+    # Plot the waveform
+    plt.figure(figsize=(30, 10))
+    plt.plot(time, audio_data, color='blue')
+    plt.title('Audio Waveform')
+    plt.xlabel('Time (s)')
+    plt.ylabel('Amplitude')
+    # plt.savefig('audio_waveframe.png')
+    plot = st.pyplot(plt)
+
+    return plot
+
+
+# def spectrogram(file_path):
+#     # Compute the short-time Fourier transform (STFT)
+#     n_fft = 500  # Number of FFT points 2048
+#     hop_length = 1  # Hop length for STFT 512
+#     audio_data, sampling_rate = librosa.load(file_path)
+#     stft = librosa.stft(audio_data, n_fft=n_fft, hop_length=hop_length)
+#     # Convert the magnitude spectrogram to decibels (log scale)
+#     spectrogram = librosa.amplitude_to_db(np.abs(stft))
+#     # Plot the spectrogram
+#     plt.figure(figsize=(30, 10))
+#     # librosa.display.specshow(spectrogram, sr=sampling_rate, hop_length=hop_length, x_axis='time', y_axis='linear')
+#     librosa.display.specshow(spectrogram, sr=sampling_rate, hop_length=hop_length)
+#     plt.colorbar(format='%+2.0f dB')
+#     plt.title('Spectrogram')
+#     plt.xlabel('Time (s)')
+#     plt.ylabel('Frequency (Hz)')
+#     plt.tight_layout()
+#     # plt.savefig('spectrogram.png')
+#     plot = st.pyplot(plt)
+
+#     return plot
+
+
+def spectrogram(file_path):
+    
+    y, sr = librosa.load(file_path)
+    # Compute the spectrogram
+    D = librosa.stft(y)
+    # Convert magnitude spectrogram to decibels
+    DB = librosa.amplitude_to_db(abs(D))
+    # Plot the spectrogram
+    plt.figure(figsize=(20, 5))
+    librosa.display.specshow(DB, sr=sr, x_axis='time', y_axis='hz')
+    plt.colorbar(format='%+2.0f dB')
+    plt.title('Spectrogram')
+    plt.xlabel('Time (s)')
+    plt.ylabel('Frequency (Hz)')
+    plt.tight_layout()
+    plot = st.pyplot(plt)
+
+    return plot
+
+
+
+def audio_signals(file_path):
+    aw = audio_waveframe(file_path)
+    spg = spectrogram(file_path)
+  
+    return aw, spg

audio_processing.py

@@ -0,0 +1,52 @@
+'''Copyright 2024 Ashok Kumar
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.'''
+
+
+import os
+import matplotlib.pyplot as plt
+plt.style.use('dark_background')
+import seaborn as sns
+import numpy as np
+import librosa
+from IPython.display import Audio
+import pandas as pd
+
+
+# Function to extract features from audio file
+def extract_features(file_path):
+    # Load audio file
+    audio, sample_rate = librosa.load(file_path)
+    # Extract features using Mel-Frequency Cepstral Coefficients (MFCC)
+    mfccs = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)
+    # Flatten the features into a 1D array
+    flattened_features = np.mean(mfccs.T, axis=0)
+    
+    return flattened_features
+
+# Function to load dataset and extract features
+def load_data_and_extract_features(data_dir):
+    labels = []
+    features = []
+    # Loop through each audio file in the dataset directory
+    for filename in os.listdir(data_dir):
+        if filename.endswith('.wav'):
+            file_path = os.path.join(data_dir, filename)
+            # Extract label from filename
+            label = filename.split('-')[0]
+            labels.append(label)
+            # Extract features from audio file
+            feature = extract_features(file_path)
+            features.append(feature)
+            
+    return np.array(features), np.array(labels)

audio_record.py

@@ -0,0 +1,60 @@
+'''Copyright 2024 Ashok Kumar
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.'''
+
+
+
+import pyaudio
+import wave
+
+def record_audio(output_filename, duration=5, sample_rate=44100, channels=2, chunk=1024, format=pyaudio.paInt16):
+    audio = pyaudio.PyAudio()
+
+    # Open recording stream
+    stream = audio.open(format=format,
+                        channels=channels,
+                        rate=sample_rate,
+                        input=True,
+                        frames_per_buffer=chunk)
+
+    print("Recording...")
+
+    frames = []
+
+    # Record for the specified duration
+    for i in range(0, int(sample_rate / chunk * duration)):
+        data = stream.read(chunk)
+        frames.append(data)
+
+    print("Finished recording.")
+
+    # Stop and close the stream
+    stream.stop_stream()
+    stream.close()
+    audio.terminate()
+
+    # Save the recorded audio to a WAV file
+    with wave.open(output_filename, 'wb') as wf:
+        wf.setnchannels(channels)
+        wf.setsampwidth(audio.get_sample_size(format))
+        wf.setframerate(sample_rate)
+        wf.writeframes(b''.join(frames))
+
+# output_filename = "recorded_audio.wav"
+# record_audio(output_filename, duration=5)
+# print(f"Audio recorded and saved as {output_filename}")
+
+# Example usage
+# output_filename = "recorded_audio.wav"
+# record_audio(output_filename, duration=5)
+# print(f"Audio recorded and saved as {output_filename}")

base/llm.py

@@ -0,0 +1,70 @@
+import os
+from dotenv import load_dotenv
+import json
+import streamlit as st
+from huggingface_hub import InferenceApi, login, InferenceClient
+
+# 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

base/main.py

@@ -0,0 +1,179 @@
+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!')
+

bird_audio_classification_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76253985cd575fc581063035f509e85f36b20b1efd950ed48a907f8892c592d2
+size 6231152

classes.json

@@ -0,0 +1 @@
+{"66": "Ornate Tinamou_sound", "59": "Moluccan Megapode_sound", "76": "Red-throated Piping Guan_sound", "51": "Little Chachalaca_sound", "102": "Undulated Tinamou_sound", "6": "Baudo Guan_sound", "110": "White-crested Guan_sound", "33": "Dusky Megapode_sound", "12": "Black-capped Tinamou_sound", "8": "Berlepschs Tinamou_sound", "50": "Lesser Rhea_sound", "83": "Scaled Chachalaca_sound", "32": "Darwins Nothura_sound", "58": "Micronesian Megapode_sound", "113": "Yellow-legged Tinamou_sound", "84": "Slaty-breasted Tinamou_sound", "7": "Bearded Guan_sound", "96": "Tataupa Tinamou_sound", "45": "Grey-legged Tinamou_sound", "86": "Solitary Tinamou_sound", "13": "Black-fronted Piping Guan_sound", "14": "Blue-throated Piping Guan_sound", "97": "Tawny-breasted Tinamou_sound", "70": "Plain Chachalaca_sound", "71": "Puna Tinamou_sound", "82": "Rusty-margined Guan_sound", "23": "Chestnut-winged Chachalaca_sound", "88": "Southern Brown Kiwi_sound", "104": "Variegated Tinamou_sound", "28": "Colombian Chachalaca_sound", "111": "White-throated Tinamou_sound", "74": "Red-faced Guan_sound", "63": "Northern Cassowary_sound", "57": "Melanesian Megapode_sound", "101": "Trinidad Piping Guan_sound", "49": "Lesser Nothura_sound", "80": "Rufous-vented Chachalaca_sound", "112": "White-winged Guan_sound", "77": "Red-winged Tinamou_sound", "79": "Rufous-headed Chachalaca_sound", "20": "Chaco Chachalaca_sound", "37": "East Brazilian Chachalaca_sound", "64": "Okarito Kiwi_sound", "99": "Thicket Tinamou_sound", "69": "Philippine Megapode_sound", "103": "Vanuatu Megapode_sound", "39": "Emu_sound", "100": "Tongan Megapode_sound", "89": "Southern Cassowary_sound", "19": "Cauca Guan_sound", "94": "Taczanowskis Tinamou_sound", "40": "Great Spotted Kiwi_sound", "109": "White-browed Guan_sound", "56": "Marail Guan_sound", "60": "New Guinea Scrubfowl_sound", "4": "Barred Tinamou_sound", "30": "Crested Guan_sound", "106": "West Mexican Chachalaca_sound", "47": "Hooded Tinamou_sound", "48": "Huayco Tinamou_sound", "98": "Tepui Tinamou_sound", "44": "Grey-headed Chachalaca_sound", "78": "Rufous-bellied Chachalaca_sound", "15": "Brazilian Tinamou_sound", "61": "Nicobar Megapode_sound", "22": "Chestnut-headed Chachalaca_sound", "24": "Chilean Tinamou_sound", "26": "Cinereous Tinamou_sound", "38": "Elegant Crested Tinamou_sound", "27": "Collared Brushturkey_sound", "10": "Black Tinamou_sound", "65": "Orange-footed Scrubfowl_sound", "0": "Andean Guan_sound", "53": "Little Tinamou_sound", "3": "Band-tailed Guan_sound", "54": "Maleo_sound", "29": "Common Ostrich_sound", "107": "White-bellied Chachalaca_sound", "31": "Curve-billed Tinamou_sound", "43": "Grey Tinamou_sound", "5": "Bartletts Tinamou_sound", "95": "Tanimbar Megapode_sound", "87": "Somali Ostrich_sound", "21": "Chestnut-bellied Guan_sound", "16": "Brown Tinamou_sound", "35": "Dwarf Cassowary_sound", "11": "Black-billed Brushturkey_sound", "68": "Patagonian Tinamou_sound", "17": "Brushland Tinamou_sound", "18": "Buff-browed Chachalaca_sound", "42": "Greater Rhea_sound", "93": "Sula Megapode_sound", "91": "Spixs Guan_sound", "81": "Rusty Tinamou_sound", "75": "Red-legged Tinamou_sound", "67": "Pale-browed Tinamou_sound", "2": "Australian Brushturkey_sound", "1": "Andean Tinamou_sound", "55": "Malleefowl_sound", "36": "Dwarf Tinamou_sound", "46": "Highland Tinamou_sound", "85": "Small-billed Tinamou_sound", "25": "Choco Tinamou_sound", "9": "Biak Scrubfowl_sound", "34": "Dusky-legged Guan_sound", "62": "North Island Brown Kiwi_sound", "73": "Red-billed Brushturkey_sound", "105": "Wattled Brushturkey_sound", "108": "White-bellied Nothura_sound", "92": "Spotted Nothura_sound", "52": "Little Spotted Kiwi_sound", "72": "Quebracho Crested Tinamou_sound", "41": "Great Tinamou_sound", "90": "Speckled Chachalaca_sound"}

extracted_features.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:234f3c08bc2031bac5be26d7e65549357a4a304ddb14739f26fb1508144b562d
+size 1728928

requirements.txt

@@ -0,0 +1,15 @@
+streamlit==1.37.0
+librosa==0.10.1
+joblib==1.3.2
+ipython==8.17.2
+matplotlib==3.7.2
+matplotlib-inline==0.1.6
+scikit-learn==1.5.0
+numpy==1.25.2
+seaborn==0.12.2
+pandas==2.2.0
+tensorflow==2.17.0
+wikipedia==1.4.0
+huggingface-hub==0.26.2
+Wikipedia-API==0.7.1
+python-dotenv==1.0.1