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| import gradio as gr | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM | |
| from google.colab import userdata | |
| import gtts | |
| # === Load Pipeline === | |
| sentiment_pipeline = pipeline("sentiment-analysis", verbose = 0, model="distilbert/distilbert-base-uncased-finetuned-sst-2-english") | |
| # === Sentiment Analysis === | |
| def analyze_sentiment(text): | |
| result = sentiment_pipeline(text)[0] | |
| return f"{result['label']} (Score: {result['score']:.2f})" | |
| # Initialize the summarization pipeline, tokenizer, and model | |
| model_name = "sshleifer/distilbart-cnn-12-6" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| summary_pipe = pipeline("summarization", model=model, tokenizer=tokenizer) | |
| # Function to chunk text with an overlap | |
| def chunk_text_with_overlap(tokens, max_length, overlap): | |
| chunks = [] | |
| for i in range(0, len(tokens), max_length - overlap): | |
| chunk = tokens[i:i + max_length] | |
| chunks.append(chunk) | |
| return chunks | |
| # === Summarization === | |
| def summarize_text(text): | |
| # Get the maximum length from the model configuration | |
| max_length = model.config.max_position_embeddings | |
| print('max_length:', max_length) | |
| # Define the overlap | |
| overlap = 50 # Adjust overlap as needed | |
| # Tokenize the text | |
| tokens = tokenizer(text, return_tensors='pt', truncation=False)['input_ids'][0] | |
| # Chunk the tokens with overlap | |
| chunks = chunk_text_with_overlap(tokens, max_length, overlap) | |
| # Summarize each chunk | |
| summaries = [] | |
| for chunk in chunks: | |
| input_ids = chunk.unsqueeze(0) # Add batch dimension | |
| summary_ids = model.generate(input_ids, max_length=max_length, num_beams=4, length_penalty=2.0, early_stopping=True) | |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| summaries.append(summary) | |
| # Combine the summaries into a final summary | |
| final_summary = ' '.join(summaries) | |
| return final_summary | |
| # === Text-to-Speech === | |
| def text_to_speech(text): | |
| tts = gtts.gTTS(text) | |
| tts.save("output.mp3") | |
| return "output.mp3" | |
| chat_pipeline = pipeline("text-generation", model="yasserrmd/Human-Like-Qwen2.5-1.5B-Instruct") | |
| # === Chatbot === | |
| def chatbot(message, chat_history): | |
| # Generate response | |
| result = chat_pipeline(message, max_new_tokens=10) | |
| # Extract only the reply | |
| bot_reply = result[0]["generated_text"] | |
| return bot_reply | |
| chat_pipeline1 = pipeline("text-generation", model="yasserrmd/Human-Like-Qwen2.5-1.5B-Instruct") | |
| # === Chatbot === | |
| def chatbot1(message, chat_history): | |
| # Generate response | |
| result = chat_pipeline1(message, max_new_tokens=10) | |
| # Extract only the reply | |
| bot_reply = result[0]["generated_text"] | |
| return bot_reply | |
| # === Build Gradio Interface === | |
| with gr.Blocks() as demo: | |
| with gr.Tabs(): | |
| with gr.Tab("Sentiment Analysis"): | |
| gr.Markdown("### 🔍 Sentiment Analysis") | |
| sentiment_input = gr.Textbox(label="Enter text", lines=3, placeholder="Type a sentence to analyze...") | |
| sentiment_button = gr.Button("Analyze") | |
| sentiment_output = gr.Textbox(label="Sentiment") | |
| sentiment_button.click(analyze_sentiment, inputs=sentiment_input, outputs=sentiment_output) | |
| with gr.Tab("Summarization"): | |
| gr.Markdown("### ✂️ Summarization") | |
| summary_input = gr.Textbox(label="Enter text", lines=8, placeholder="Paste long text here...") | |
| summary_button = gr.Button("Summarize") | |
| summary_output = gr.Textbox(label="Summary") | |
| summary_button.click(summarize_text, inputs=summary_input, outputs=summary_output) | |
| with gr.Tab("Text-to-Speech"): | |
| gr.Markdown("### 🗣️ Text-to-Speech (using Bark)") | |
| tts_input = gr.Textbox(label="Enter text to speak", lines=3, placeholder="Try something like: 'Hello, how are you?'") | |
| tts_button = gr.Button("Generate Speech") | |
| tts_output = gr.Audio(label="Generated Speech", type="numpy") | |
| tts_button.click(text_to_speech, inputs=tts_input, outputs=tts_output) | |
| with gr.TabItem("Chatbot 1"): | |
| gr.ChatInterface( | |
| chatbot, | |
| type="messages", | |
| title="Chatbot 1", | |
| description="Start the conversation in Chatbot 1!") | |
| with gr.TabItem("Chatbot 2"): | |
| gr.ChatInterface( | |
| chatbot1, | |
| type="messages", | |
| title="Chatbot 2", | |
| description="Start the conversation in Chatbot 2!") | |
| demo.launch() |