Update main.py

main.py

@@ -34,14 +34,6 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-nlp_qa = pipeline("document-question-answering", model="jinhybr/OCR-DocVQA-Donut")
-nlp_qa_v2 = pipeline("document-question-answering", model="faisalraza/layoutlm-invoices", ignore_mismatched_sizes=True)
-nlp_qa_v3 = pipeline("question-answering", model="deepset/roberta-base-squad2")
-nlp_classification = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
-nlp_classification_v2 = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
-nlp_speech_to_text = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
-nlp_sequence_classification = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
-nlp_main_classification = pipeline("zero-shot-classification", model="roberta-large-mnli")
 code_generation_model = pipeline('text-generation', model='codeparrot/codeparrot-small')
 
 description = """
@@ -55,38 +47,6 @@ This API performs document question answering using a LayoutLMv2-based model.
 
 app = FastAPI(docs_url="/", description=description)
 
-@app.post("/uploadfile/", description="Upload an image file to extract text and answer provided questions.")
-async def perform_document_qa(
-    file: UploadFile = File(...),
-    questions: str = Form(...),
-):
-    try:
-        # Read the uploaded file as bytes
-        contents = await file.read()
-
-        # Open the image using PIL
-        image = Image.open(BytesIO(contents))
-
-        # Perform document question answering for each question using LayoutLMv2-based model
-        answers_dict = {}
-        for question in questions.split(','):
-            result = nlp_qa(
-                image,
-                question.strip()
-            )
-
-            # Access the 'answer' key from the first item in the result list
-            answer = result[0]['answer']
-
-            # Format the question as a string without extra characters
-            formatted_question = question.strip("[]")
-
-            answers_dict[formatted_question] = answer
-
-        return answers_dict
-    except Exception as e:
-        return JSONResponse(content=f"Error processing file: {str(e)}", status_code=500)
-
 @app.post("/generate_code/", description="Generate code based on the provided prompt.")
 async def generate_code(prompt: str = Form(...)):
     try: