Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from transformers import pipeline | |
| import torch | |
| from huggingface_hub import InferenceClient | |
| # Initialize pipeline | |
| sentiment_analyzer = pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis") | |
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| chatbot = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| speech_model = pipeline("automatic-speech-recognition", model="openai/whisper-small") | |
| # Sentiment analysis functoin that takes text and returns sentiment and confidence | |
| def analyze_sentiment(text): | |
| result = sentiment_analyzer(text)[0] | |
| return f"Sentiment: {result['label']}", f"Confidence: {result['score']:.2f}" | |
| # Function where users can engage in a conversational interface. | |
| def chat_response( | |
| message, | |
| max_tokens = 500, | |
| temperature = 0.8, | |
| top_p = 0.8, | |
| ): | |
| messages = [{"role": "system", "content": 'You are an assistant for a company called TrailTrek Gears Co specialized in Hiking'}] | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| for message in chatbot.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| # A summarization function | |
| def summarize_text(text): | |
| # Ensure text is within model's max length | |
| max_length = 1024 | |
| if len(text.split()) > max_length: | |
| text = " ".join(text.split()[:max_length]) | |
| summary = summarizer(text, max_length=130, min_length=30, do_sample=False) | |
| return summary[0]['summary_text'] | |
| # A function that takes text and outputs a speech of the text | |
| def speech_to_text(audio): | |
| if audio is None: | |
| return "Please upload an audio file or record audio to transcribe." | |
| # Transcribe the audio | |
| result = speech_model(audio) | |
| return result["text"] | |
| with gr.Blocks(title="TrailTrek Gears AI Suite") as demo: | |
| gr.Markdown("# TrailTrek Gears AI Suite") | |
| with gr.Tab("Sentiment Analysis"): | |
| gr.Markdown("### Analyze the sentiment of your text") | |
| with gr.Row(): | |
| sentiment_input = gr.Textbox(label="Enter text to analyze", lines=4) | |
| with gr.Column(): | |
| sentiment_label = gr.Textbox(label="Sentiment") | |
| confidence_score = gr.Textbox(label="Confidence Score") | |
| sentiment_button = gr.Button("Analyze Sentiment") | |
| sentiment_button.click( | |
| analyze_sentiment, | |
| inputs=sentiment_input, | |
| outputs=[sentiment_label, confidence_score] | |
| ) | |
| with gr.Tab("Chatbot"): | |
| gr.Markdown("# Stateless Chatbot using Zephyr-7b-beta") | |
| with gr.Row(): | |
| chat_input = gr.Textbox( | |
| placeholder="Type your message here...", | |
| label="Your Message" | |
| ) | |
| chat_output = gr.Textbox( | |
| label="Assistant Response", | |
| interactive=False | |
| ) | |
| chat_input.submit(chat_response, inputs=chat_input, outputs=chat_output) | |
| with gr.Tab("Summarization"): | |
| gr.Markdown("### Get a concise summary of your text") | |
| with gr.Row(): | |
| summary_input = gr.Textbox(label="Enter text to summarize", lines=8) | |
| summary_output = gr.Textbox(label="Summary", lines=4) | |
| summary_button = gr.Button("Generate Summary") | |
| summary_button.click( | |
| summarize_text, | |
| inputs=summary_input, | |
| outputs=summary_output | |
| ) | |
| with gr.Tab("Speech-to-Text"): | |
| gr.Markdown("### Convert speech to text using Whisper") | |
| with gr.Row(): | |
| with gr.Column(): | |
| audio = gr.Audio( | |
| "microphone", | |
| type="filepath", | |
| label="Record or upload audio" | |
| ) | |
| transcribe_button = gr.Button("Transcribe Audio") | |
| text_output = gr.Textbox( | |
| label="Transcribed Text", | |
| lines=4, | |
| placeholder="Transcribed text will appear here..." | |
| ) | |
| transcribe_button.click( | |
| speech_to_text, | |
| inputs=audio, | |
| outputs=text_output | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| demo.launch() |