Create app.py

app.py

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+import gradio as gr
+from theme4 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/Phi-4-Hindi"
+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)
+
+# Set up logging folder and CommitScheduler to push logs to Hugging Face dataset repo
+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/phi-gradio-logs",  
+    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'?"], 
+                ["टि�� 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 get and 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()