Upload 3 files

Dockerfile

@@ -0,0 +1,27 @@
+FROM python:3.10.12
+COPY main.py /app/
+COPY requirements.txt /app/
+RUN pip install --no-cache-dir -r /app/requirements.txt 
+WORKDIR /app
+CMD [ "uvicorn ","main:app","--host","0.0.0.0","--port","80" ]
+
+
+FROM python:3.10.12
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file to the container
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install -r requirements.txt
+
+# Copy the rest of the project files to the container
+COPY main.py .
+
+# Expose the port that the application will be running on
+EXPOSE 8000
+
+# Run the application
+CMD ["uvicorn", "main:app"]

main.py

@@ -0,0 +1,79 @@
+from fastapi import FastAPI, HTTPException
+import uvicorn
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer, util
+
+app = FastAPI()
+
+# Load the SentenceTransformer model
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+class StringInput(BaseModel):
+    sentence1: str
+    sentence2: str
+
+class ListInput(BaseModel):
+    sentences1: list
+    sentences2: list
+
+class MatrixInput(BaseModel):
+    matrix: list
+
+# Configure CORS settings
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # You should restrict this to specific origins in production
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.post("/calculate-cosine-similarity-tabulated")
+async def calculate_cosine_similarity_tabulated(data: ListInput):
+    try:
+        # Encode the sentences
+        embeddings1 = model.encode(data.sentences1)
+        embeddings2 = model.encode(data.sentences2)
+
+        # Calculate cosine similarity scores
+        similarity_table = []
+
+        # Add the first row with the specified headers
+        first_row = [""] + ["PO1", "PO2", "PO3", "PO4", "PO5", "PO6", "PO7", "PO8", "PO9", "PO10", "PO11", "PO12", "PSO1", "PSO2", "PSO3"]
+        similarity_table.append(first_row)
+
+        # Iterate through the sentences and calculate similarity scores
+        for i, sentence1 in enumerate(embeddings1):
+            row = ["S" + str(i + 1)]  # Start the row with "S1", "S2", ...
+
+            for sentence2 in embeddings2:
+                score = util.pytorch_cos_sim(sentence1, sentence2).item()
+                formatted_score = f"{score:.3f}"
+                row.append(formatted_score)
+
+            # Add the row to the similarity table
+            similarity_table.append(row)
+
+        return {"cosine_similarity_table": similarity_table}
+    except Exception as e:
+        # Handle any exceptions that might occur during calculation
+        raise HTTPException(status_code=500, detail="An error occurred during calculation")
+
+@app.post("/calculate-cosine-similarity")
+async def calculate_cosine_similarity(data: StringInput):
+    try:
+        # Encode the sentences
+        my_embedding = model.encode(data.sentence1)
+        embeddings = model.encode(data.sentence2)
+
+        # Calculate cosine similarity
+        cos_sim = util.pytorch_cos_sim(my_embedding, embeddings).item()
+
+        return {"cosine_similarity": cos_sim}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="An error occurred during calculation")
+    
+
+if __name__ == "__main__":
+    uvicorn.run(app,port=8000)

requirements.txt

@@ -0,0 +1,6 @@
+fastapi
+uvicorn
+pydantic
+sentence-transformers
+
+