First commit

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,85 @@
+# EcoWise Chatbot
+
+🌍 **EcoWise** is a climate-savvy chatbot designed to answer questions related to climate change, sustainability, and carbon footprint using a language model fine-tuned for these topics. 
+
+## Overview
+
+The EcoWise chatbot leverages a Keras NLP model hosted on Hugging Face to provide intelligent responses to user queries. This tool aims to educate and inform users on important environmental issues.
+
+## Features
+
+- Interactive chat interface using Gradio.
+- Responses generated by an AI model fine-tuned on climate-related data.
+- Environmentally focused, providing insight into sustainability and climate change.
+
+## Prerequisites
+
+Before running the application, ensure you have the following:
+
+- Python 3.7 or later
+- A valid Hugging Face API key. You can obtain one by creating an account on [Hugging Face](https://huggingface.co/) and visiting your account settings.
+
+## Installation
+
+1. **Clone the repository**:
+
+```bash
+git clone https://huggingface.co/spaces/MNLobago/ECOWISE
+cd ECOWISE
+```
+
+2. **Install the required packages**:
+
+You can install the necessary packages using pip. If you have a `requirements.txt` file, run:
+
+```bash
+pip install -r requirements.txt
+```
+
+If you don't have a `requirements.txt`, you can install the required packages like this:
+
+```bash
+pip install gradio keras-nlp huggingface_hub
+```
+
+## Setting Up the Environment
+
+To run the application, you need to set up your Hugging Face API key as an environment variable.
+
+On your command line, set the environment variable before running the app:
+
+```bash
+export HUGGINGFACE_API_KEY="your_api_key_here"
+```
+
+Replace `"your_api_key_here"` with your actual Hugging Face API key.
+
+## Running the Application
+
+After installing all dependencies and setting the environment variable, you can launch the chatbot with the following command:
+
+```bash
+python app.py
+```
+
+This will start a Gradio web interface, where you can interact with the EcoWise chatbot directly.
+
+## Using the Chatbot
+
+1. Open the provided local URL in your web browser (usually http://127.0.0.1:7860).
+2. Type your questions about climate change, sustainability, or carbon footprint into the chat interface.
+3. Receive responses generated by the model.
+
+## Code Structure
+
+- `app.py`: Main application file that initializes and runs the EcoWise chatbot.
+- `requirements.txt`: (If present) Lists all the required packages for the application.
+
+## License
+
+This project is licensed under the MIT License. See the LICENSE file for details.
+
+## Acknowledgments
+
+- [Hugging Face](https://huggingface.co/): For providing the model and API services.
+- [Gradio](https://gradio.app/): For creating beautiful user interfaces easily.

.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,65 @@
+import os
+import gc
+import psutil
+import gradio as gr
+import keras_nlp
+from huggingface_hub import login
+
+# Get the API key from environment variable
+api_key = os.getenv("HUGGINGFACE_API_KEY")
+if not api_key:
+    raise ValueError("Please set the 'HUGGINGFACE_API_KEY' environment variable.")
+
+# Log in with the provided Hugging Face API token
+login(api_key)
+
+# Load the Keras NLP model from Hugging Face
+model_path = "MNLobago/EcoWise_model"
+gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(f"hf://{model_path}")
+
+class GemmaChat:
+    def __init__(self, model, max_length=150, system=""):
+        self.model = model
+        self.max_length = max_length
+        self.system = system
+        self.history = []
+
+    def get_full_prompt(self, user_input):
+        return f"User: {user_input}\nModel:"
+
+    def query(self, question):
+        if not self.history:
+            prompt = self.system + "\n" + self.get_full_prompt(question) if self.system else self.get_full_prompt(question)
+        else:
+            prompt = self.get_full_prompt(question)
+        
+        response = self.model.generate(prompt, max_length=self.max_length)
+        model_response = response.replace(prompt, "").strip()
+        
+        # Sanitize the response
+        if model_response.endswith('?'):
+            model_response = model_response.rstrip('?') + '.'
+
+        gc.collect()
+        return model_response
+
+# Initialize the chat object
+chat = GemmaChat(
+    model=gemma_lm,
+    system="""You are an intelligent chatbot focused on answering questions related to climate change, sustainability, and carbon footprint."""
+)
+
+def chat_with_model(input_text):
+    chat.history = []
+    answer = chat.query(input_text)
+    return [("user", input_text), ("model", answer)]
+
+# Create and launch the Gradio interface
+demo = gr.Interface(
+    fn=chat_with_model,
+    inputs="text",
+    outputs="chatbot",
+    description="🌍 Welcome to EcoWise, your go-to climate-savvy chatbot! I'm here to help you."
+)
+
+demo.launch()

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -0,0 +1,5 @@
+huggingface_hub==0.25.2
+gradio
+keras-nlp
+huggingface-hub
+psutil

README.md

@@ -1,14 +1,85 @@
----
-title: ECOWISE
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: EcoWiseis a climate-savvy chatbot dedicated to helping you r
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+# EcoWise Chatbot
+
+🌍 **EcoWise** is a climate-savvy chatbot designed to answer questions related to climate change, sustainability, and carbon footprint using a language model fine-tuned for these topics. 
+
+## Overview
+
+The EcoWise chatbot leverages a Keras NLP model hosted on Hugging Face to provide intelligent responses to user queries. This tool aims to educate and inform users on important environmental issues.
+
+## Features
+
+- Interactive chat interface using Gradio.
+- Responses generated by an AI model fine-tuned on climate-related data.
+- Environmentally focused, providing insight into sustainability and climate change.
+
+## Prerequisites
+
+Before running the application, ensure you have the following:
+
+- Python 3.7 or later
+- A valid Hugging Face API key. You can obtain one by creating an account on [Hugging Face](https://huggingface.co/) and visiting your account settings.
+
+## Installation
+
+1. **Clone the repository**:
+
+```bash
+git clone https://huggingface.co/spaces/MNLobago/ECOWISE
+cd ECOWISE
+```
+
+2. **Install the required packages**:
+
+You can install the necessary packages using pip. If you have a `requirements.txt` file, run:
+
+```bash
+pip install -r requirements.txt
+```
+
+If you don't have a `requirements.txt`, you can install the required packages like this:
+
+```bash
+pip install gradio keras-nlp huggingface_hub
+```
+
+## Setting Up the Environment
+
+To run the application, you need to set up your Hugging Face API key as an environment variable.
+
+On your command line, set the environment variable before running the app:
+
+```bash
+export HUGGINGFACE_API_KEY="your_api_key_here"
+```
+
+Replace `"your_api_key_here"` with your actual Hugging Face API key.
+
+## Running the Application
+
+After installing all dependencies and setting the environment variable, you can launch the chatbot with the following command:
+
+```bash
+python app.py
+```
+
+This will start a Gradio web interface, where you can interact with the EcoWise chatbot directly.
+
+## Using the Chatbot
+
+1. Open the provided local URL in your web browser (usually http://127.0.0.1:7860).
+2. Type your questions about climate change, sustainability, or carbon footprint into the chat interface.
+3. Receive responses generated by the model.
+
+## Code Structure
+
+- `app.py`: Main application file that initializes and runs the EcoWise chatbot.
+- `requirements.txt`: (If present) Lists all the required packages for the application.
+
+## License
+
+This project is licensed under the MIT License. See the LICENSE file for details.
+
+## Acknowledgments
+
+- [Hugging Face](https://huggingface.co/): For providing the model and API services.
+- [Gradio](https://gradio.app/): For creating beautiful user interfaces easily.

app.py

@@ -1,64 +1,65 @@
+import os
+import gc
+import psutil
 import gradio as gr
-from huggingface_hub import InferenceClient
+import keras_nlp
+from huggingface_hub import login
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Get the API key from environment variable
+api_key = os.getenv("HUGGINGFACE_API_KEY")
+if not api_key:
+    raise ValueError("Please set the 'HUGGINGFACE_API_KEY' environment variable.")
 
+# Log in with the provided Hugging Face API token
+login(api_key)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Load the Keras NLP model from Hugging Face
+model_path = "MNLobago/EcoWise_model"
+gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(f"hf://{model_path}")
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+class GemmaChat:
+    def __init__(self, model, max_length=150, system=""):
+        self.model = model
+        self.max_length = max_length
+        self.system = system
+        self.history = []
 
-    messages.append({"role": "user", "content": message})
+    def get_full_prompt(self, user_input):
+        return f"User: {user_input}\nModel:"
 
-    response = ""
+    def query(self, question):
+        if not self.history:
+            prompt = self.system + "\n" + self.get_full_prompt(question) if self.system else self.get_full_prompt(question)
+        else:
+            prompt = self.get_full_prompt(question)
+        
+        response = self.model.generate(prompt, max_length=self.max_length)
+        model_response = response.replace(prompt, "").strip()
+        
+        # Sanitize the response
+        if model_response.endswith('?'):
+            model_response = model_response.rstrip('?') + '.'
 
-    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
+        gc.collect()
+        return model_response
 
-        response += token
-        yield response
+# Initialize the chat object
+chat = GemmaChat(
+    model=gemma_lm,
+    system="""You are an intelligent chatbot focused on answering questions related to climate change, sustainability, and carbon footprint."""
+)
 
+def chat_with_model(input_text):
+    chat.history = []
+    answer = chat.query(input_text)
+    return [("user", input_text), ("model", answer)]
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+# Create and launch the Gradio interface
+demo = gr.Interface(
+    fn=chat_with_model,
+    inputs="text",
+    outputs="chatbot",
+    description="🌍 Welcome to EcoWise, your go-to climate-savvy chatbot! I'm here to help you."
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()

requirements.txt

@@ -1 +1,5 @@
-huggingface_hub==0.25.2
+huggingface_hub==0.25.2
+gradio
+keras-nlp
+huggingface-hub
+psutil