..

Dockerfile

@@ -2,9 +2,21 @@ FROM python:3.9-slim
 
 WORKDIR /code
 
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    python3-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first to leverage Docker cache
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
+# Copy the rest of the application
 COPY . .
 
-CMD ["python", "app.py"]
+# Expose port for Gradio
+EXPOSE 7860
+
+# Command to run the application
+CMD ["python", "app.py"]

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Model initialization
+model_name = "gpt2"  # or your specific model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Add special tokens for languages
+special_tokens = {"additional_special_tokens": ["[YORUBA]", "[IGBO]", "[HAUSA]"]}
+tokenizer.add_special_tokens(special_tokens)
+model.resize_token_embeddings(len(tokenizer))
+
+# Move model to GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+def generate_text(prompt, language):
+    # Add language tag to prompt
+    tagged_prompt = f"[{language.upper()}] {prompt}"
+    
+    # Tokenize
+    inputs = tokenizer(tagged_prompt, return_tensors="pt").to(device)
+    
+    # Generate
+    outputs = model.generate(
+        **inputs,
+        max_length=100,
+        num_return_sequences=1,
+        temperature=0.7,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    # Decode and return
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="Enter your prompt"),
+        gr.Dropdown(choices=["YORUBA", "IGBO", "HAUSA"], label="Select Language")
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="Nigerian Language Generator",
+    description="Generate text in Yoruba, Igbo, or Hausa using a fine-tuned GPT model."
+)
+
+if __name__ == "__main__":
+    iface.launch()