Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Sage Mental Health Bot
-emoji: 🌍
-colorFrom: yellow
-colorTo: gray
+title: Sage-Mental-Health-Bot
+app_file: main.py
 sdk: gradio
 sdk_version: 5.5.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

@@ -0,0 +1,348 @@
+import gradio as gr
+import os
+import json
+from dotenv import load_dotenv
+import requests
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from huggingface_hub import login
+from datetime import datetime
+import numpy as np
+import torch
+from gtts import gTTS
+import tempfile
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
+import torch
+
+# Load environment variables from .env file
+load_dotenv()
+token = os.getenv("HF_TOKEN")
+
+# Use the token in the login function
+login(token=token)
+# File paths for storing model configurations and chat history
+MODEL_CONFIG_FILE = "model_config.json"
+CHAT_HISTORY_FILE = "chat_history.json"
+
+# Load model configurations from a JSON file (if exists)
+def load_model_config():
+    if os.path.exists(MODEL_CONFIG_FILE):
+        with open(MODEL_CONFIG_FILE, 'r') as f:
+            return json.load(f)
+    return {
+        "gpt-4": {
+            "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2024-08-01-preview",
+            "api_key": os.getenv("GPT4_API_KEY"),
+            "model_path": None  # No model path for API models
+        },
+        "gpt-4o": {
+            "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview",
+            "api_key": os.getenv("GPT4O_API_KEY"),
+            "model_path": None
+        },
+        "gpt-35-turbo": {
+            "endpoint": "https://rogerkoranteng.openai.azure.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2024-08-01-preview",
+            "api_key": os.getenv("GPT35_TURBO_API_KEY"),
+            "model_path": None
+        },
+        "gpt-4-32k": {
+            "endpoint": "https://roger-m38orjxq-australiaeast.openai.azure.com/openai/deployments/gpt-4-32k/chat/completions?api-version=2024-08-01-preview",
+            "api_key": os.getenv("GPT4_32K_API_KEY"),
+            "model_path": None
+        }
+    }
+
+predefined_messages = {
+    "feeling_sad": "Hello, I am feeling sad today, what should I do?",
+    "Nobody likes me": "Hello, Sage. I feel like nobody likes me. What should I do?",
+    'Boyfriend broke up': "Hi Sage, my boyfriend broke up with me. I'm feeling so sad. What should I do?",
+    'I am lonely': "Hi Sage, I am feeling lonely. What should I do?",
+    'I am stressed': "Hi Sage, I am feeling stressed. What should I do?",
+    'I am anxious': "Hi Sage, I am feeling anxious. What should I do?",
+}
+
+# Save model configuration to JSON
+def save_model_config():
+    with open(MODEL_CONFIG_FILE, 'w') as f:
+        json.dump(model_config, f, indent=4)
+
+# Load chat history from a JSON file
+def load_chat_history():
+    if os.path.exists(CHAT_HISTORY_FILE):
+        with open(CHAT_HISTORY_FILE, 'r') as f:
+            return json.load(f)
+    return []
+
+# Save chat history to a JSON file
+def save_chat_history(chat_history):
+    with open(CHAT_HISTORY_FILE, 'w') as f:
+        json.dump(chat_history, f, indent=4)
+
+# Define model configurations
+model_config = load_model_config()
+
+# Function to dynamically add downloaded model to model_config
+def add_downloaded_model(model_name, model_path):
+    model_config[model_name] = {
+        "endpoint": None,
+        "model_path": model_path,
+        "api_key": None
+    }
+    save_model_config()
+    return list(model_config.keys())
+
+# Function to download model from Hugging Face synchronously
+def download_model(model_name):
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        model_path = f"./models/{model_name}"
+        os.makedirs(model_path, exist_ok=True)
+        model.save_pretrained(model_path)
+        tokenizer.save_pretrained(model_path)
+        updated_models = add_downloaded_model(model_name, model_path)
+        return f"Model '{model_name}' downloaded and added.", updated_models
+    except Exception as e:
+        return f"Error downloading model '{model_name}': {e}", list(model_config.keys())
+
+# Chat function using the selected model
+def generate_response(model_choice, user_message, chat_history):
+    model_info = model_config.get(model_choice)
+    if not model_info:
+        return "Invalid model selection. Please choose a valid model.", chat_history
+
+    chat_history.append({"role": "user", "content": user_message})
+    headers = {"Content-Type": "application/json"}
+
+    # Check if the model is an API model (it will have an endpoint)
+    if model_info["endpoint"]:
+        if model_info["api_key"]:
+            headers["api-key"] = model_info["api_key"]
+
+        data = {"messages": chat_history, "max_tokens": 1500, "temperature": 0.7}
+
+        try:
+            # Send request to the API model endpoint
+            response = requests.post(model_info["endpoint"], headers=headers, json=data)
+            response.raise_for_status()
+            assistant_message = response.json()['choices'][0]['message']['content']
+            chat_history.append({"role": "assistant", "content": assistant_message})
+            save_chat_history(chat_history)  # Save chat history to JSON
+        except requests.exceptions.RequestException as e:
+            assistant_message = f"Error: {e}"
+            chat_history.append({"role": "assistant", "content": assistant_message})
+            save_chat_history(chat_history)
+    else:
+        # If it's a local model, load the model and tokenizer from the local path
+        model_path = model_info["model_path"]
+        try:
+            tokenizer = AutoTokenizer.from_pretrained(model_path)
+            model = AutoModelForCausalLM.from_pretrained(model_path)
+
+            inputs = tokenizer(user_message, return_tensors="pt")
+            outputs = model.generate(inputs['input_ids'], max_length=500, num_return_sequences=1)
+            assistant_message = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+            chat_history.append({"role": "assistant", "content": assistant_message})
+            save_chat_history(chat_history)
+        except Exception as e:
+            assistant_message = f"Error loading model locally: {e}"
+            chat_history.append({"role": "assistant", "content": assistant_message})
+            save_chat_history(chat_history)
+
+    # Convert the assistant message to audio
+    tts = gTTS(assistant_message)
+    audio_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
+    tts.save(audio_file.name)
+
+    return chat_history, audio_file.name
+
+# Function to format chat history with custom bubble styles
+def format_chat_bubble(history):
+    formatted_history = ""
+    for message in history:
+        timestamp = datetime.now().strftime("%H:%M:%S")
+        if message["role"] == "user":
+            formatted_history += f'''
+                <div class="user-bubble">
+                    <strong>Me:</strong> {message["content"]}
+                </div>
+            '''
+        else:
+            formatted_history += f'''
+                <div class="assistant-bubble">
+                    <strong>Sage:</strong> {message["content"]}
+                </div>
+            '''
+    return formatted_history
+
+tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+
+def transcribe(audio):
+    if audio is None:
+        return "No audio input received."
+
+    sr, y = audio
+
+    # Convert to mono if stereo
+    if y.ndim > 1:
+        y = y.mean(axis=1)
+
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+
+    # Tokenize the audio
+    input_values = tokenizer(y, return_tensors="pt", sampling_rate=sr).input_values
+
+    # Perform inference
+    with torch.no_grad():
+        logits = model(input_values).logits
+
+    # Decode the logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = tokenizer.decode(predicted_ids[0])
+
+    return transcription
+
+# Create the Gradio interface
+with gr.Blocks() as interface:
+    gr.Markdown("## Chat with Sage - Your Mental Health Advisor")
+
+    with gr.Tab("Model Management"):
+        with gr.Tabs():
+            with gr.TabItem("Model Selection"):
+                gr.Markdown("### Select Model for Chat")
+                model_dropdown = gr.Dropdown(choices=list(model_config.keys()), label="Choose a Model", value="gpt-4",
+                                             allow_custom_value=True)
+                status_textbox = gr.Textbox(label="Model Selection Status", value="Selected model: gpt-4")
+                model_dropdown.change(lambda model: f"Selected model: {model}", inputs=model_dropdown,
+                                      outputs=status_textbox)
+
+            with gr.TabItem("Download Model"):  # Sub-tab for downloading models
+                gr.Markdown("### Download a Model from Hugging Face")
+                model_name_input = gr.Textbox(label="Enter Model Name from Hugging Face (e.g., gpt2)")
+                download_button = gr.Button("Download Model")
+                download_status = gr.Textbox(label="Download Status")
+
+                # Model download synchronous handler
+                def on_model_download(model_name):
+                    download_message, updated_models = download_model(model_name)
+                    # Trigger the dropdown update to show the newly added model
+                    return download_message, gr.update(choices=updated_models, value=updated_models[-1])
+
+                download_button.click(on_model_download, inputs=model_name_input,
+                                      outputs=[download_status, model_dropdown])
+
+                refresh_button = gr.Button("Refresh Model List")
+                refresh_button.click(lambda: gr.update(choices=list(model_config.keys())), inputs=[],
+                                     outputs=model_dropdown)
+
+    with gr.Tab("Chat Interface"):
+        gr.Markdown("### Chat with Sage")
+
+        # Chat history state for tracking conversation
+        chat_history_state = gr.State(load_chat_history())  # Load existing chat history
+
+        # Add initial introduction message
+        if not chat_history_state.value:
+            chat_history_state.value.append({"role": "assistant", "content": "Hello, I am Sage. How can I assist you today?"})
+
+        chat_display = gr.HTML(label="Chat", value=format_chat_bubble(chat_history_state.value), elem_id="chat-display")
+
+        user_message = gr.Textbox(placeholder="Type your message here...", label="Your Message")
+        send_button = gr.Button("Send Message")
+
+        # Predefined message buttons
+        predefined_buttons = [gr.Button(value=msg) for msg in predefined_messages.values()]
+
+        # Real-time message updating
+        def update_chat(model_choice, user_message, chat_history_state):
+            chat_history, audio_file = generate_response(model_choice, user_message, chat_history_state)
+            formatted_chat = format_chat_bubble(chat_history)
+            return formatted_chat, chat_history, audio_file
+
+        send_button.click(
+            update_chat,
+            inputs=[model_dropdown, user_message, chat_history_state],
+            outputs=[chat_display, chat_history_state, gr.Audio(autoplay=True)]
+        )
+
+        send_button.click(lambda: "", None, user_message)  # Clears the user input after sending
+
+        # Add click events for predefined message buttons
+        for button, message in zip(predefined_buttons, predefined_messages.values()):
+            button.click(
+                update_chat,
+                inputs=[model_dropdown, gr.State(message), chat_history_state],
+                outputs=[chat_display, chat_history_state, gr.Audio(autoplay=True)]
+            )
+
+    with gr.Tab("Speech Interface"):
+        gr.Markdown("### Speak with Sage")
+
+        audio_input = gr.Audio(type="numpy")
+        transcribe_button = gr.Button("Transcribe")
+        transcribed_text = gr.Textbox(label="Transcribed Text")
+
+        transcribe_button.click(
+            transcribe,
+            inputs=audio_input,
+            outputs=transcribed_text
+        )
+
+        send_speech_button = gr.Button("Send Speech Message")
+
+        send_speech_button.click(
+            update_chat,
+            inputs=[model_dropdown, transcribed_text, chat_history_state],
+            outputs=[chat_display, chat_history_state, gr.Audio(autoplay=True)]
+        )
+
+    # Add custom CSS for scrolling chat box and bubbles
+    interface.css = """
+        #chat-display {
+            max-height: 500px;
+            overflow-y: auto;
+            padding: 10px;
+            background-color: #1a1a1a;
+            border-radius: 10px;
+            display: flex;
+            flex-direction: column;
+            justify-content: flex-start;
+            box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
+            scroll-behavior: smooth;
+        }
+
+        /* User message style - text only */
+        .user-bubble {
+            color: #ffffff;  /* Text color for the user */
+            padding: 8px 15px;
+            margin: 8px 0;
+            word-wrap: break-word;
+            align-self: flex-end;
+            font-size: 14px;
+            position: relative;
+            max-width: 70%;  /* Make the bubble width dynamic */
+            border-radius: 15px;
+            background-color: #121212;  /* Light cyan background for the user */
+            transition: color 0.3s ease;
+        }
+
+        /* Assistant message style - text only */
+        .assistant-bubble {
+            color: #ffffff;  /* Text color for the assistant */
+            padding: 8px 15px;
+            margin: 8px 0;
+            word-wrap: break-word;
+            align-self: flex-start;
+            background-color: #2a2a2a;
+            font-size: 14px;
+            position: relative;
+            max-width: 70%;
+            transition: color 0.3s ease;
+        }
+
+    """
+
+# Launch the Gradio interface
+interface.launch(server_name="0.0.0.0", server_port=8080, share=True)

requirements.txt

@@ -0,0 +1,170 @@
+aiofiles==23.2.1
+aiohappyeyeballs==2.4.3
+aiohttp==3.10.10
+aiosignal==1.3.1
+analytics-python==1.4.post1
+annotated-types==0.7.0
+anyio==3.7.1
+asgiref==3.8.1
+async-timeout==4.0.3
+attrs==24.2.0
+backoff==1.10.0
+bcrypt==4.2.0
+build==1.2.2.post1
+cachetools==5.5.0
+certifi==2024.8.30
+cffi==1.17.1
+charset-normalizer==3.4.0
+chroma-bullet==2.2.0
+chroma-hnswlib==0.7.6
+chroma-migrate==0.0.7
+chromadb==0.5.18
+click==8.1.7
+clickhouse-connect==0.6.6
+coloredlogs==15.0.1
+contourpy==1.3.0
+cryptography==43.0.3
+cycler==0.12.1
+Deprecated==1.2.14
+duckdb==0.7.1
+durationpy==0.9
+exceptiongroup==1.2.2
+fastapi==0.115.4
+ffmpeg==1.4
+ffmpy==0.4.0
+filelock==3.16.1
+flatbuffers==24.3.25
+fonttools==4.54.1
+frozenlist==1.5.0
+fsspec==2024.10.0
+google-auth==2.36.0
+googleapis-common-protos==1.65.0
+gradio==5.5.0
+gradio_client==1.4.2
+grpcio==1.67.1
+gTTS==2.5.4
+h11==0.14.0
+HLL==2.2.0
+httpcore==1.0.6
+httptools==0.6.4
+httpx==0.27.2
+huggingface-hub==0.26.2
+humanfriendly==10.0
+idna==3.10
+importlib_metadata==8.5.0
+importlib_resources==6.4.5
+Jinja2==3.1.4
+joblib==1.4.2
+kiwisolver==1.4.7
+kubernetes==31.0.0
+linkify-it-py==2.0.3
+lz4==4.3.3
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.2
+mdit-py-plugins==0.4.2
+mdurl==0.1.2
+mmh3==5.0.1
+monotonic==1.6
+more-itertools==10.5.0
+mpmath==1.3.0
+multidict==6.1.0
+networkx==3.4.2
+nltk==3.9.1
+numpy==1.23.5
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==9.1.0.70
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-cusparselt-cu12==0.6.2
+nvidia-nccl-cu12==2.21.5
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-nvtx-cu12==12.1.105
+oauthlib==3.2.2
+onnxruntime==1.20.0
+opentelemetry-api==1.28.1
+opentelemetry-exporter-otlp-proto-common==1.28.1
+opentelemetry-exporter-otlp-proto-grpc==1.28.1
+opentelemetry-instrumentation==0.49b1
+opentelemetry-instrumentation-asgi==0.49b1
+opentelemetry-instrumentation-fastapi==0.49b1
+opentelemetry-proto==1.28.1
+opentelemetry-sdk==1.28.1
+opentelemetry-semantic-conventions==0.49b1
+opentelemetry-util-http==0.49b1
+orjson==3.10.11
+overrides==7.7.0
+packaging==24.2
+pandas==1.5.3
+paramiko==3.5.0
+pillow==11.0.0
+plotly==5.14.0
+posthog==3.7.0
+propcache==0.2.0
+protobuf==5.28.3
+pulsar-client==3.5.0
+pyasn1==0.6.1
+pyasn1_modules==0.4.1
+pycparser==2.22
+pycryptodome==3.21.0
+pydantic==2.9.2
+pydantic_core==2.23.4
+pydub==0.25.1
+Pygments==2.18.0
+PyNaCl==1.5.0
+pyparsing==3.2.0
+PyPika==0.48.9
+pyproject_hooks==1.2.0
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-multipart==0.0.12
+pytorch-triton==3.1.0+cf34004b8a
+pytz==2024.2
+PyYAML==6.0.2
+regex==2024.11.6
+requests==2.32.3
+requests-oauthlib==2.0.0
+rich==13.9.4
+rsa==4.9
+ruff==0.7.3
+safehttpx==0.1.1
+safetensors==0.4.5
+scikit-learn==1.1.3
+scipy==1.14.1
+semantic-version==2.10.0
+sentence-transformers==3.2.1
+sentencepiece==0.2.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.1
+starlette==0.41.2
+sympy==1.13.1
+tenacity==9.0.0
+threadpoolctl==3.5.0
+tokenizers==0.20.3
+tomli==2.0.2
+tomlkit==0.12.0
+torch==2.6.0.dev20241107+cu121
+torchaudio==2.5.0.dev20241107+cu121
+torchvision==0.20.0.dev20241107+cu121
+tqdm==4.67.0
+transformers==4.46.2
+triton==3.1.0
+typer==0.13.0
+typing_extensions==4.12.2
+uc-micro-py==1.0.3
+urllib3==2.2.3
+uvicorn==0.32.0
+uvloop==0.21.0
+watchfiles==0.24.0
+websocket-client==1.8.0
+websockets==12.0
+wrapt==1.16.0
+yarl==1.17.1
+zipp==3.20.2
+zstandard==0.23.0