Create main.py

main.py

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+import os
+# cwd = os.getcwd()
+# cachedir = cwd+'/cache'
+# os.mkdir(cachedir)
+# os.environ['TRANSFORMERS_CACHE'] = "/app/yo/cache"
+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-mpnet-base-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 = ["CO" + str(i + 1)]  # Start the row with "S1", "S2", ...
+
+            for sentence2 in embeddings2:
+                score = util.pytorch_cos_sim(sentence1, sentence2).item()
+                score = max(score, 0)
+                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")