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Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Create cache directory with proper permissions
+RUN mkdir -p /app/model_cache && chmod 777 /app/model_cache
+
+# Copy application code
+COPY . .
+
+# Expose the port your FastAPI app will run on
+EXPOSE 7860
+
+# Command to run the application
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+# Simple implementation for translation using the BART model
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import BartTokenizer, BartForConditionalGeneration
+
+app = FastAPI()
+
+# Define request model
+class TranslationRequest(BaseModel):
+    text: str
+    max_length: int = 150
+    min_length: int = 40
+
+# Download and cache the model during initialization
+# This happens only once when the app starts
+try:
+    # Explicitly download to a specific directory with proper error handling
+    cache_dir = "./model_cache"
+    model_name = "facebook/bart-large-cnn"
+    
+    print(f"Loading tokenizer from {model_name}...")
+    tokenizer = BartTokenizer.from_pretrained(model_name, cache_dir=cache_dir, local_files_only=False)
+    
+    print(f"Loading model from {model_name}...")
+    model = BartForConditionalGeneration.from_pretrained(model_name, cache_dir=cache_dir, local_files_only=False)
+    
+    print("Model and tokenizer loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {str(e)}")
+    raise
+
+@app.post("/summarize/")
+async def translate_text(request: TranslationRequest):
+    # Process the input text
+    inputs = tokenizer(request.text, return_tensors="pt", max_length=1024, truncation=True)
+    
+    # Generate summary
+    summary_ids = model.generate(
+        inputs["input_ids"],
+        max_length=request.max_length, 
+        min_length=request.min_length,
+        num_beams=4,
+        length_penalty=2.0,
+        early_stopping=True
+    )
+    
+    # Decode the generated summary
+    translation = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    
+    return {"summary": translation}
+
+# Basic health check endpoint
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy", "model": "facebook/bart-large-cnn"}

requirements.txt

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+fastapi>=0.95.0
+uvicorn>=0.21.1
+transformers>=4.27.0
+torch>=2.0.0
+pydantic>=1.10.7