first commit

Dockerfile

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+FROM python:3.10.9
+COPY . .
+
+WORKDIR /
+
+RUN mkdir -p /.cache/huggingface/hub
+
+# Separate RUN command for chmod
+RUN chmod -R 777 /.cache/huggingface
+
+RUN pip install --no-cache-dir --upgrade -r /requirements.txt
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, Body
+from typing import Dict
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware, 
+    allow_origins=['*'], 
+    allow_credentials=True,
+    allow_methods=["*"], 
+    allow_headers=["*"]
+)
+
+def load_model():
+    peft_model_id = "ANWAR101/lora-bart-base-youtube-cnn"
+    config = PeftConfig.from_pretrained(peft_model_id) 
+    model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)
+    tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+    model = PeftModel.from_pretrained(model, peft_model_id)
+    return model , tokenizer
+
+
+model , tokenizer = load_model()
+
+@app.post("/summarize")
+async def summarize(data: Dict[str, str] = Body(...)):
+    """Summarize a text using the loaded Peft model."""
+
+    text = data.get("text")
+
+    # Check for missing text
+    if not text:
+        return {"error": "Missing text in request body"}, 400
+
+    # Preprocess the text
+    inputs = tokenizer(text, truncation=True, return_tensors="pt")
+
+    # Generate summary using the model
+    outputs = model.generate(
+        **inputs, max_length=300, min_length=50, do_sample=True, num_beams=3,
+        no_repeat_ngram_size=2, temperature=0.6, length_penalty=1.0
+    )
+
+    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response = {"summary": summary}
+    return response

requirements.txt

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+transformers==4.38.2
+peft
+fastapi
+uvicorn 
+huggingface_hub