Add application file

Dockerfile

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+FROM python:3.9
+
+WORKDIR /app
+
+COPY . /app/
+
+RUN pip install --no-cache-dir -r requirements.txt 
+
+RUN pip install fastapi uvicorn 
+
+EXPOSE 8080
+CMD ["uvicorn", "app:app", "--reload", "--host", "0.0.0.0", "--port", "8080"]

app.py

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+#load package
+from transformers import pipeline
+from fastapi import FastAPI,HTTPException,status,UploadFile,File
+from pydantic import BaseModel
+import uvicorn
+import logging
+from PIL import Image
+import pytesseract
+from io import BytesIO
+
+#Additional information
+ 
+Informations = """ 
+-text : Texte à resumé
+
+output:
+- Text summary : texte resumé
+"""
+
+
+
+app =FastAPI(
+    title='Text Summary',
+    description =Informations
+)
+
+#class to define the input text
+logging.basicConfig(level=logging.INFO)
+logger =logging.getLogger(__name__)
+summarize =pipeline('summarization', model="facebook/bart-large-cnn")
+generated_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large") 
+translation_pieline =pipeline("translation_en_to_fr",model="Helsinki-NLP/opus-mt-en-fr")
+classify_zero_shot = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
+class TextSummary(BaseModel):
+    text:str
+
+#ENDPOINT
+
+@app.get("/")
+async def home():
+    return 'STN BIG DATA'
+
+
+@app.post("/summary")
+async def summary_text_bart(input:TextSummary):
+    "add text to summarize"
+    try:
+        summary = summarize(input.text,do_sample=False)
+        summary_text =summary[0].get('summary_text')
+        result = classify_zero_shot(
+                summary_text,
+                candidate_labels=["En Cours", "Non traiter", "Terminer"],
+                hypothesis_template="Cet Resumé est sur {}."
+            )
+        print(f"[iNFO] Input data as text:\n{summary_text}")
+        logger.info(f"[INFO] input data:{input.text}")
+        logger.info(f"[INFO] Summary:{summary}")
+        formatted_result = [
+            f"{label}: {score:.2f}" for label, score in zip(result['labels'], result['scores']*100)
+        ]
+        return {
+            "summary_text" :summary_text,
+            "Statut":formatted_result,
+            # "format":formatted_result,
+            "len_input" : len(input.text),
+            "len_output" :len(summary_text)
+
+        }
+         
+
+    except ValueError as e:
+        logger.error(f"valueError:{e}")
+        return {"error ER":str(e)}
+    
+    except Exception as e:
+        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,detail="Could not summarize the input text.")
+    
+@app.post("/upload-image/")
+async def upload_image(image: UploadFile = File(...)):
+    contents = await image.read()
+    try:
+        image_open = Image.open(BytesIO(contents))
+        raw_text = pytesseract.image_to_string(image_open,lang='fra')
+        summary = summarize(raw_text,do_sample=False)
+        summary_text =summary[0].get('summary_text')
+        return {
+                # "text": raw_text,
+                "summary":summary_text}
+    except Exception as e:
+        return {"error": str(e)}
+if __name__ == "__main__":
+    uvicorn.run("main:app",host="0.0.0.0",port=8000,reload=True)
+
+
+
+

requirements.txt

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+fastapi==0.111.0
+torch==2.3.1
+transformers==4.41.2
+uvicorn==0.30.1
+pydantic==2.7.4
+pillow==10.3.0
+numpy==1.23.5
+scipy==1.11.3
+sentencepiece==0.2.0
+pytesseract==0.3.10
+Pillow==8.0.0