simple modelo

.gitignore

@@ -0,0 +1 @@
+.env

app.py

@@ -1,25 +1,140 @@
-from transformers import pipeline
-import gradio as gr
+import streamlit as st
 import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import os
+from threading import Thread
+import requests
+import time
 
-# Cargar un modelo de texto de Hugging Face (puede ser de completado, traducción, etc.)
-dispositivo = 0 if torch.cuda.is_available() else -1
-modelo = pipeline("text-generation", model="microsoft/Phi-4-multimodal-instruct", device=dispositivo, trust_remote_code=True)  # Cambia el modelo si lo deseas
-
-# Función para generar texto
-def generar_texto(prompt):
-    resultado = modelo(prompt, max_length=100, do_sample=True)
-    return resultado[0]["generated_text"]
-
-# Interfaz con Gradio
-interfaz = gr.Interface(
-    fn=generar_texto,
-    inputs=gr.Textbox(label="Introduce tu texto"),
-    outputs=gr.Textbox(label="Texto generado"),
-    title="Generador de Texto con Hugging Face",
-    description="Escribe un prompt y el modelo generará un texto basado en él."
-)
-
-# Ejecutar la app
-if __name__ == "__main__":
-    interfaz.launch()
+# Define model path for caching (Avoids reloading every app restart)
+MODEL_PATH = "/mnt/data/Phi-4-Hindi"
+TOKEN = os.environ.get("HF_TOKEN")
+MODEL_NAME = "DrishtiSharma/Phi-4-Hindi-quantized"
+
+# Load Model & Tokenizer Once
+@st.cache_resource()
+def load_model():
+    with st.spinner("Loading model... Please wait ⏳"):
+        try:
+            if not os.path.exists(MODEL_PATH):
+                model = AutoModelForCausalLM.from_pretrained(
+                    MODEL_NAME, token=TOKEN, trust_remote_code=True, torch_dtype=torch.bfloat16
+                )
+                tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=TOKEN)
+                model.save_pretrained(MODEL_PATH)
+                tokenizer.save_pretrained(MODEL_PATH)
+            else:
+                model = AutoModelForCausalLM.from_pretrained(MODEL_PATH)
+                tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+        except requests.exceptions.ConnectionError:
+            st.error("⚠️ Connection error! Unable to download the model. Please check your internet connection and try again.")
+            return None, None
+        except requests.exceptions.ReadTimeout:
+            st.error("⚠️ Read Timeout! The request took too long. Please try again later.")
+            return None, None
+    
+    return model, tokenizer
+
+# Load and move model to appropriate device
+model, tok = load_model()
+if model is None or tok is None:
+    st.stop()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+try:
+    model = model.to(device)
+except torch.cuda.OutOfMemoryError:
+    st.error("⚠️ CUDA Out of Memory! Running on CPU instead.")
+    device = torch.device("cpu")
+    model = model.to(device)
+
+terminators = [tok.eos_token_id]
+
+# Initialize session state if not set
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# Chat function
+def chat(message, temperature, do_sample, max_tokens):
+    """Processes chat input and generates a response using the model."""
+    
+    # Append new message to history
+    st.session_state.chat_history.append({"role": "user", "content": message})
+
+    # Convert chat history into model-friendly format
+    messages = tok.apply_chat_template(st.session_state.chat_history, tokenize=False, add_generation_prompt=True)
+    model_inputs = tok([messages], return_tensors="pt").to(device)
+    
+    # Initialize streamer for token-wise response
+    streamer = TextIteratorStreamer(tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+    
+    # Define generation parameters
+    generate_kwargs = {
+        "inputs": model_inputs["input_ids"],
+        "streamer": streamer,
+        "max_new_tokens": max_tokens,
+        "do_sample": do_sample,
+        "temperature": temperature,
+        "eos_token_id": terminators,
+    }
+    
+    if temperature == 0:
+        generate_kwargs["do_sample"] = False
+
+    # Generate response asynchronously
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    
+    # Collect response as it streams
+    response_text = ""
+    for new_text in streamer:
+        response_text += new_text
+        yield response_text
+
+    # Save the assistant's response to session history
+    st.session_state.chat_history.append({"role": "assistant", "content": response_text})
+
+# UI Setup
+st.title("💬 Chat With Phi-4-Hindi")
+st.success("✅ Model is READY to chat!")
+st.markdown("Chat with [large-traversaal/Phi-4-Hindi](https://huggingface.co/large-traversaal/Phi-4-Hindi)")
+
+# Sidebar Chat Settings
+temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.3, 0.1)
+do_sample = st.sidebar.checkbox("Use Sampling", value=True)
+max_tokens = st.sidebar.slider("Max Tokens", 128, 4096, 512, 1)
+text_color = st.sidebar.selectbox("Text Color", ["Red", "Black", "Blue", "Green", "Purple"], index=0)
+dark_mode = st.sidebar.checkbox("🌙 Dark Mode", value=False)
+
+# Function to format chat messages
+def get_html_text(text, color):
+    return f'<p style="color: {color.lower()}; font-size: 16px;">{text}</p>'
+
+# Display chat history
+for msg in st.session_state.chat_history:
+    role = "👤" if msg["role"] == "user" else "🤖"
+    st.markdown(get_html_text(f"**{role}:** {msg['content']}", text_color if role == "🤖" else "black"), unsafe_allow_html=True)
+
+# User Input Handling
+user_input = st.text_input("Type your message:", "")
+
+if st.button("Send"):
+    if user_input.strip():
+        st.session_state.chat_history.append({"role": "user", "content": user_input})
+        
+        # Display chatbot response
+        with st.spinner("Generating response... 🤖💭"):
+            response_generator = chat(user_input, temperature, do_sample, max_tokens)
+            final_response = ""
+            for output in response_generator:
+                final_response = output  # Store latest output
+        
+        st.success("✅ Response generated!")
+        # Add generated response to session state
+        st.rerun()
+
+if st.button("🧹 Clear Chat"):
+    with st.spinner("Clearing chat history..."):
+        st.session_state.chat_history = []
+    st.success("✅ Chat history cleared!")
+    st.rerun()

requirements.txt

@@ -1,5 +1,7 @@
-gradio
+spaces
 torch
-transformers
-requests
-peft
+git+https://github.com/huggingface/transformers/
+optimum
+accelerate
+bitsandbytes
+scipy