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ohoud_alghassab_(trailtrek_gears_co)_demo.py

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+# -*- coding: utf-8 -*-
+"""ohoud alghassab (TrailTrek Gears Co) Demo
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/18P3OPYyZb-cVbGrzgPU1MkwwjhutvrmM
+"""
+
+!pip install transformers gradio gtts huggingface_hub
+
+import gradio as gr
+from transformers import pipeline
+from huggingface_hub import InferenceClient, login
+from google.colab import userdata
+import gtts
+
+from transformers import pipeline
+
+# Load sentiment analysis model
+sentiment_pipeline = pipeline(
+    "sentiment-analysis",
+    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english"
+)
+
+# Define sentiment analysis function
+def analyze_sentiment(text):
+    result = sentiment_pipeline(text)[0]
+    label = result["label"]
+    score = result["score"] * 100
+    return f"{label} — {score:.2f}%"
+
+from huggingface_hub import InferenceClient, login
+import gradio as gr
+from google.colab import userdata
+
+# Secure login
+HF_TOKEN = userdata.get("HF_TOKEN")
+login(token=HF_TOKEN)
+
+# Load Mistral model
+client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
+
+# Response generator
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    messages = [{"role": "system", "content": system_message}]
+    for user_msg, bot_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if bot_msg:
+            messages.append({"role": "assistant", "content": bot_msg})
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+    for chunk in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = chunk.choices[0].delta.content or ""
+        response += token
+        yield response
+
+from transformers import pipeline
+
+# Load summarization pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Define summarization function
+def summarize_text(text):
+    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+    return summary[0]["summary_text"]
+
+import gtts
+
+# Define text-to-speech function
+def text_to_speech(text):
+    tts = gtts.gTTS(text)
+    tts.save("output.mp3")
+    return "output.mp3"
+
+"""*🚀* Gradio Multi-Tab Interface
+(final step to connect all functions above into UI)
+"""
+
+import gradio as gr
+
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple")) as demo:
+    gr.Image(
+        value="https://www.leaders-mena.com/leaders/uploads/2024/09/Tuwaiq-Meta-780x470.jpg",
+        show_label=False,
+        show_download_button=False,
+        height=120
+    )
+
+    gr.Markdown(
+        "<h1 style='text-align: center; color: orange;'>🌟 TrailTrek Gears - All-in-One AI App</h1>",
+        elem_id="main-title"
+    )
+
+    # 📊 Sentiment Analysis Tab
+    with gr.Tab("📊 Sentiment Analysis"):
+        sentiment_input = gr.Textbox(label="Enter your text")
+        sentiment_output = gr.Textbox(label="Sentiment Result")
+        sentiment_btn = gr.Button("Analyze Sentiment")
+        sentiment_btn.click(analyze_sentiment, inputs=sentiment_input, outputs=sentiment_output)
+
+    # 💬 Chatbot Tab
+    with gr.Tab("💬 Chatbot"):
+        gr.Markdown("### Simple Chat with Mistral-7B")
+        chatbot_output = gr.Textbox(label="Chat History", lines=10, interactive=False, show_copy_button=True)
+        chatbot_input = gr.Textbox(label="Your Message", placeholder="Type something...")
+        chatbot_history = gr.State([])
+
+        def custom_chat_simple(user_input, history):
+            if history is None:
+                history = []
+            system_message = "You are a helpful and polite assistant."
+            gen = respond(user_input, history, system_message, 256, 0.5, 0.95)
+            final_response = ""
+            for chunk in gen:
+                final_response = chunk
+            history.append((user_input, final_response))
+            chat_display = ""
+            for user, bot in history:
+                chat_display += f"🧑: {user}\n🤖: {bot}\n\n"
+            return chat_display, history
+
+        send_button = gr.Button("Send")
+        send_button.click(fn=custom_chat_simple, inputs=[chatbot_input, chatbot_history], outputs=[chatbot_output, chatbot_history])
+
+    # ✂️ Summarization Tab
+    with gr.Tab("✂️ Summarization"):
+        input_text = gr.Textbox(lines=8, label="Enter long text")
+        output_summary = gr.Textbox(label="Summary")
+        summarize_btn = gr.Button("Summarize")
+        summarize_btn.click(summarize_text, inputs=input_text, outputs=output_summary)
+
+    # 🔈 Text-to-Speech Tab
+    with gr.Tab("🔈 Text-to-Speech"):
+        tts_input = gr.Textbox(label="Enter text to convert to audio")
+        tts_output = gr.Audio(label="Generated Speech")
+        tts_btn = gr.Button("Generate Audio")
+        tts_btn.click(text_to_speech, inputs=tts_input, outputs=tts_output)
+
+demo.launch()

requirements.txt

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+transformers
+gradio
+gtts
+huggingface_hub
+Torch
+google.colab