changes in main

main.py

@@ -1,48 +1,106 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from sentence_transformers import SentenceTransformer
-import numpy as np
-
-# Initialize the FastAPI app
-app = FastAPI()
-
-# Load the pre-trained SentenceTransformer model
-model = SentenceTransformer("Alibaba-NLP/gte-base-en-v1.5", trust_remote_code=True)
-
-# Define the request body schema
-class TextInput(BaseModel):
-    text: str
-
-# Home route
-@app.get("/")
-async def home():
-    return {"message": "welcome to home page"}
-
-# Define the API endpoint for generating embeddings
-@app.post("/embed")
-async def generate_embedding(text_input: TextInput):
-    """
-    Generate a 768-dimensional embedding for the input text.
-    Returns the embedding in a structured format with rounded values.
-    """
-    try:
-        # Generate the embedding
-        embedding = model.encode(text_input.text, convert_to_tensor=True).cpu().numpy()
-
-        # Round embedding values to 2 decimal places
-        rounded_embedding = np.round(embedding, 2).tolist()
-
-        # Return structured response
-        return {
-            "dimensions": len(rounded_embedding),
-            "embeddings": [rounded_embedding]
-        }
-
-    except Exception as e:
-        # Handle any errors
-        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
-
-# Run the FastAPI app
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer,util
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+import uvicorn
+import numpy as np
+import pandas as pd
+
+
+# Initialize the FastAPI app
+app = FastAPI()
+
+# Load the pre-trained SentenceTransformer model
+model = SentenceTransformer("Alibaba-NLP/gte-base-en-v1.5", trust_remote_code=True)
+
+# Define the request body schema
+class TextInput(BaseModel):
+    text: str
+
+# Home route
+@app.get("/")
+async def home():
+    return {"message": "welcome to home page"}
+
+# Define the API endpoint for generating embeddings
+@app.post("/embed")
+async def generate_embedding(text_input: TextInput):
+    """
+    Generate a 768-dimensional embedding for the input text.
+    Returns the embedding in a structured format with rounded values.
+    """
+    try:
+        # Generate the embedding
+        embedding = model.encode(text_input.text, convert_to_tensor=True).cpu().numpy()
+
+        # Round embedding values to 2 decimal places
+        rounded_embedding = np.round(embedding, 2).tolist()
+
+        # Return structured response
+        return {
+            "dimensions": len(rounded_embedding),
+            "embeddings": [rounded_embedding]
+        }
+
+    except Exception as e:
+        # Handle any errors
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+    
+
+# Load pre-trained SentenceTransformer model
+model = SentenceTransformer("Alibaba-NLP/gte-base-en-v1.5", trust_remote_code=True)
+
+# Train the Logistic Regression model during app startup
+df = pd.read_excel("sms_process_data_main.xlsx")
+X_train, X_test, y_train, y_test = train_test_split(df["MessageText"], df["label"], test_size=0.2, random_state=42)
+X_train_embeddings = model.encode(X_train.tolist())
+
+# Initialize and train the Logistic Regression model
+logreg_model = LogisticRegression(max_iter=100)
+logreg_model.fit(X_train_embeddings, y_train)
+
+# Define input schema
+class TextInput(BaseModel):
+    text: str
+
+@app.post("/predict")
+async def generate_prediction(text_input: TextInput):
+    """
+    Predict the label for the given text input using the trained model.
+    """
+    try:
+        # Generate embedding for the input text
+        new_embedding = model.encode([text_input.text])
+
+        # Predict the label using the trained Logistic Regression model
+        prediction = logreg_model.predict(new_embedding).tolist()[0]  # Extract single prediction
+
+        # Return structured response
+        return {
+            
+            "predicted_label": prediction
+        }
+    except Exception as e:
+        # Handle any errors
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+    
+class SentencesInput(BaseModel):
+    sentence1: str
+    sentence2: str
+@app.post("/text_to_tensor")
+def text_to_tensor(input: SentencesInput):
+    try:
+        # Generate embeddings
+        embeddings = model.encode([input.sentence1, input.sentence2])
+
+        # Compute cosine similarity
+        cosine_similarity = util.cos_sim(embeddings[0], embeddings[1]).item()
+
+        return {"cosine_similarity": round(cosine_similarity, 3)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)