# -*- coding: utf-8 -*-
"""Untitled4.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1352Z_3Tsa5_YFTfI-jWhZpSJ_k4yHSm3
"""

pip install gradio transformers torch

!pip install gTTS

import gradio as gr
from transformers import pipeline, TextGenerationPipeline, AutoModelForCausalLM, AutoTokenizer
from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline
import torch
from gtts import gTTS
import tempfile

sentiment_pipeline = pipeline("sentiment-analysis")
summarizer_pipeline = pipeline("summarization")


# Sentiment Analysis
def analyze_sentiment(text):
    result = sentiment_pipeline(text)[0]
    return result["label"], round(result["score"], 3)


# Summarization
def summarize(text):
    summary = summarizer_pipeline(text, max_length=60, min_length=15, do_sample=False)
    return summary[0]["summary_text"]


def text_to_speech(text):
    tts = gTTS(text)
    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as fp:
        tts.save(fp.name)
        return fp.name


# Gradio UI
with gr.Blocks(title="TrailTrek AI Assistant",theme="soft") as demo:
    gr.Markdown("## 🧠 TrailTrek Gears Co - Multi-Task AI Demo")

    with gr.Tab("📊 Sentiment Analysis"):
        with gr.Row():
            text_input = gr.Textbox(label="Enter text")
            sentiment_output = gr.Text(label="Sentiment")
            confidence_output = gr.Number(label="Confidence")
        analyze_btn = gr.Button("Analyze")
        analyze_btn.click(analyze_sentiment, inputs=[text_input], outputs=[sentiment_output, confidence_output])


    with gr.Tab("📄 Summarization"):
        input_text = gr.Textbox(lines=8, label="Enter a long text")
        output_summary = gr.Text(label="Summary")
        summarize_btn = gr.Button("Summarize")
        summarize_btn.click(summarize, inputs=[input_text], outputs=[output_summary])


    with gr.Tab("🗣️ Text-to-Speech"):
        tts_input = gr.Textbox(label="Enter text to speak")
        tts_output = gr.Audio(label="Generated Speech", type="filepath")
        tts_btn = gr.Button("Convert to Speech")
        tts_btn.click(text_to_speech, inputs=[tts_input], outputs=[tts_output])



demo.launch()