Create TabsTask.py

TabsTask.py

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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
+
+# === Build Gradio Interface ===
+with gr.Blocks() as demo:
+    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.ChatInterface(
+            chatbot,
+            type="messages",
+            title="Chatbot",
+            description="Start the conversation by sending text messages or ask questions!",
+        )
+demo.launch()