Upload 3 files

app.py

@@ -0,0 +1,172 @@
+import gradio as gr
+from theme import fast_rtc_theme
+import torch
+import json
+import uuid
+import os
+import time
+import pytz
+from datetime import datetime
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TextIteratorStreamer,
+)
+from threading import Thread
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+import spaces
+
+os.system("apt-get update && apt-get install -y libstdc++6")
+
+# Load HF token from the environment
+token = os.environ["HF_TOKEN"]
+
+# Load Model and Tokenizer
+model_id = "large-traversaal/Mantra-14B"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    token=token,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id, token=token)
+terminators = [tokenizer.eos_token_id]
+
+# Move model to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+# Setting up logging and scheduling periodic commits to Hugging Face dataset repository with the help of CommitScheduler. 
+log_folder = Path("logs")
+log_folder.mkdir(parents=True, exist_ok=True)
+log_file = log_folder / f"chat_log_{uuid.uuid4()}.json"
+
+scheduler = CommitScheduler(
+    repo_id="DrishtiSharma/mantra-14b-user-interaction-log",  
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=0.01,  
+    token=token 
+)
+
+# Set timezone for logging timestamps
+timezone = pytz.timezone("UTC")
+
+@spaces.GPU(duration=60)
+def chat(message, history, temperature, do_sample, max_tokens, top_p):
+    start_time = time.time()
+    timestamp = datetime.now(timezone).strftime("%Y-%m-%d %H:%M:%S %Z")
+    
+    conversation_history = []
+    for item in history:
+        conversation_history.append({"role": "user", "content": item[0]})
+        if item[1] is not None:
+            conversation_history.append({"role": "assistant", "content": item[1]})
+    conversation_history.append({"role": "user", "content": message})
+    
+    messages = tokenizer.apply_chat_template(conversation_history, tokenize=False, add_generation_prompt=True)
+    model_inputs = tokenizer([messages], return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(
+        tokenizer, timeout=70.0, skip_prompt=True, skip_special_tokens=True
+    )
+
+    # Define generation parameters
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=max_tokens,
+        do_sample=do_sample,
+        temperature=temperature,
+        top_p=top_p,  
+        eos_token_id=terminators,
+    )
+
+    #Disable sampling if temperature is zero (deterministic generation)
+    if temperature == 0:
+        generate_kwargs["do_sample"] = False
+
+    generation_thread = Thread(target=model.generate, kwargs=generate_kwargs)
+    generation_thread.start()
+
+    partial_text = ""
+    for new_text in streamer:
+        partial_text += new_text
+        yield partial_text
+
+    # Calculate total response time
+    response_time = round(time.time() - start_time, 2)
+
+    # Prepare log entry for the interaction
+    log_data = {
+        "timestamp": timestamp,
+        "input": message,
+        "output": partial_text,
+        "response_time": response_time,
+        "temperature": temperature,
+        "do_sample": do_sample,
+        "max_tokens": max_tokens,
+        "top_p": top_p  
+    }
+    
+    with scheduler.lock:
+        with log_file.open("a", encoding="utf-8") as f:
+            f.write(json.dumps(log_data, ensure_ascii=False) + "\n")
+
+# Function to clear chat history
+def clear_chat():
+    return [], []
+
+# Function to export chat history as a downloadable file
+def export_chat(history):
+    if not history:
+        return None  # No chat history to export
+
+    file_path = "chat_history.txt"
+    with open(file_path, "w", encoding="utf-8") as f:
+        for msg in history:
+            f.write(f"User: {msg[0]}\nBot: {msg[1]}\n")
+    return file_path
+
+
+# Gradio UI
+with gr.Blocks(theme=fast_rtc_theme) as demo:
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("#### ⚙️🛠 Configure Settings")
+            temperature = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.1, label="Temperature", interactive=True)
+            do_sample = gr.Checkbox(label="Sampling", value=True, interactive=True)
+            max_tokens = gr.Slider(minimum=128, maximum=4096, step=1, value=1024, label="max_new_tokens", interactive=True)
+            top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.2, label="top_p", interactive=True)
+            
+        
+        with gr.Column(scale=3):
+            gr.Markdown("# **Chat With Phi-4-Hindi** 💬 ")
+            
+            chat_interface = gr.ChatInterface(
+                fn=chat,
+                examples=[
+                ["What is the English translation of: 'इस मॉडल को हिंदी और अंग्रेजी डेटा पर प्रशिक्षित किया गया था'?"], 
+                ["टिम अपने 3 बच्चों को ट्रिक या ट्रीटिंग के लिए ले जाता है। वे 4 घंटे बाहर रहते हैं। हर घंटे वे x घरों में जाते हैं। हर घर में हर बच्चे को 3 ट्रीट मिलते हैं। उसके बच्चों को कुल 180 ट्रीट मिलते हैं। अज्ञात चर x का मान क्या है?"], 
+                ["How do you play fetch? A) Throw the object for the dog to bring back to you. B) Get the object and bring it back to the dog."]
+                ],
+                additional_inputs=[temperature, do_sample, max_tokens, top_p], 
+                stop_btn="⏹ Stop",
+                description="Phi-4-Hindi is a bilingual instruction-tuned LLM for Hindi and English, trained on a mixed datasets composed of 485K Hindi-English samples.",
+                #theme="default"
+            )
+
+            with gr.Row():
+                clear_btn = gr.Button("🧹 Clear Chat", variant="primary")
+                export_btn = gr.Button("📥 Export Chat", variant="primary")
+
+            # Connect buttons to their functions (Clear and Export Chat)
+            clear_btn.click(
+                fn=clear_chat,
+                outputs=[chat_interface.chatbot, chat_interface.chatbot_value]  
+            )
+
+            export_btn.click(fn=export_chat, inputs=[chat_interface.chatbot], outputs=[gr.File()])  
+
+demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+gradio
+spaces
+torch
+optimum
+accelerate
+bitsandbytes
+git+https://github.com/huggingface/transformers/

theme.py

@@ -0,0 +1,66 @@
+import gradio as gr
+from gradio.themes.base import Base
+from gradio.themes.utils import colors, fonts, sizes
+
+# Custom Theme Class
+class FastRTCOrangeTheme(Base):
+    def __init__(
+        self,
+        *,
+        primary_hue=colors.orange,    
+        secondary_hue=colors.amber,   
+        neutral_hue=colors.gray,
+        spacing_size=sizes.spacing_md,
+        radius_size=sizes.radius_lg,
+        text_size=sizes.text_md,
+        font=(fonts.GoogleFont("Poppins"), "ui-sans-serif", "sans-serif"),
+        font_mono=(fonts.GoogleFont("Fira Code"), "ui-monospace", "monospace"),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            spacing_size=spacing_size,
+            radius_size=radius_size,
+            text_size=text_size,
+            font=font,
+            font_mono=font_mono,
+        )
+
+        super().set(
+            # Background & Layout
+            body_background_fill="linear-gradient(135deg, #FFF7ED, #FFEDD5)",  
+            body_text_color="#000000",
+
+            # Block / Panels
+            block_background_fill="white",
+            block_shadow="0 4px 12px rgba(0, 0, 0, 0.05)",
+            block_border_width="1px",
+            block_title_text_weight="700",
+
+            # Buttons (Primary: Orange)
+            button_primary_background_fill="linear-gradient(90deg, #F97316, #FB923C)",  # Orange gradient
+            button_primary_background_fill_hover="linear-gradient(90deg, #EA580C, #F97316)",
+            button_primary_text_color="white",
+            button_primary_shadow="0 2px 6px rgba(251, 146, 60, 0.4)",
+
+            # Buttons (Secondary: Subtle Neutral)
+            button_secondary_background_fill="#F8FAFC",
+            button_secondary_text_color="#1F2937",
+            button_secondary_shadow="0 1px 3px rgba(0, 0, 0, 0.1)",
+
+            # Sliders, Inputs
+            slider_color="#F97316",  # Orange slider
+            input_border_color="#E2E8F0",
+            input_border_color_focus="#F97316",
+            input_shadow="0 0 0 2px rgba(249, 115, 22, 0.2)",
+
+            # Loader & Progress
+            loader_color="#FB923C",  
+
+            # Links & Highlights
+            link_text_color="#F97316",
+            link_text_color_hover="#EA580C"
+        )
+
+fast_rtc_theme = FastRTCOrangeTheme()