dev: check working

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[
+        0
+    ]  
+    input_mask_expanded = (
+        attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    )
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
+        input_mask_expanded.sum(1), min=1e-9
+    )
+
+def cosine_similarity(u, v):
+    return F.cosine_similarity(u, v, dim=1)
+
+
+def compare(text1, text2):
+
+    sentences = [text1, text2]
+
+    tokenizer = AutoTokenizer.from_pretrained("dmlls/all-mpnet-base-v2-negation")
+    model = AutoModel.from_pretrained("dmlls/all-mpnet-base-v2-negation")
+
+    encoded_input = tokenizer(
+        sentences, padding=True, truncation=True, return_tensors="pt"
+    )
+
+    with torch.no_grad():
+        model_output = model(**encoded_input)
+
+    sentence_embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
+
+    sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+    similarity_score = cosine_similarity(
+        sentence_embeddings[0].unsqueeze(0), sentence_embeddings[1].unsqueeze(0)
+    )
+    return similarity_score.item()
+
+
+#--------------------------------------------------------------------------------------------------------------------
+from fastapi import FastAPI
+
+app = FastAPI()
+
+@app.get("/")
+def greet_json():
+    return {"Hello": "World!"}
+
+#--------------------------------------------------------------------------------------------------------------------
+
+from transformers import pipeline
+
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def Summerized_Text(text):
+    text = text.strip()
+    a = summarizer(text, max_length=130, min_length=30, do_sample=False)
+    print(a)
+    return a[0]['summary_text']
+
+#--------------------------------------------------------------------------------------------------------------------
+
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+from fastapi import FastAPI
+
+class StrRequest(BaseModel):
+    text: str
+
+
+class CompareRequest(BaseModel):
+    summary: str
+    text: str
+
+
+@app.get("/api/check")
+def check_connection():
+    try:
+        return JSONResponse(
+            {"status": 200, "message": "Message Successfully Sent"}, status_code=200
+        )
+    except Exception as e:
+        print("Error => ", e)
+        return JSONResponse({"status": 500, "message": str(e)}, status_code=500)
+
+
+@app.post("/api/summerized")
+async def get_summerized(request: StrRequest):
+    try:
+        print(request)
+        text = request.text
+        if not text:
+            return JSONResponse(
+                {"status": 422, "message": "Invalid Input"}, status_code=422
+            )
+        summary = Summerized_Text(text)
+        if "No abstract text." in summary:
+            return JSONResponse(
+                {"status": 500, "message": "No matching text found", "data": "None"}
+            )
+            
+        if not summary:
+            return JSONResponse(
+                {"status": 500, "message": "No matching text found", "data": {}}
+            )
+
+        return JSONResponse(
+            {"status": 200, "message": "Matching text found", "data": summary}
+        )
+
+    except Exception as e:
+        print("Error => ", e)
+        return JSONResponse({"status": 500, "message": str(e)}, status_code=500)
+
+
+@app.post("/api/compare")
+def compareTexts(request: CompareRequest):
+    try:
+        text = request.text
+        summary = request.summary
+        if not summary or not text:
+            return JSONResponse(
+                {"status": 422, "message": "Invalid Input"}, status_code=422
+            )
+        value = compare(text, summary)
+        return JSONResponse(
+            {
+                "status": 200,
+                "message": "Comparisons made",
+                "value": value,
+                "text": text,
+                "summary": summary,
+            }
+        )
+    except Exception as e:
+        print("Error => ", e)
+        return JSONResponse({"status": 500, "message": str(e)}, status_code=500)
+

requirements.txt

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+fastapi
+uvicorn[standard]
+torch
+torchaudio
+tensorflow
+scipy
+websockets 
+wsproto
+soundfile
+SpeechRecognition 
+pydub
+transformers
+ffmpeg
+librosa
+soundfile
+python-multipart
+matplotlib
+numpy
+google-api-python-client
+google-auth-httplib2
+google-auth-oauthlib
+gdown
+PyPDF2
+tf-keras
+requests