Added project files

DOCKERFILE

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+# Use the specific official Python 3.10.19 slim image
+FROM python:3.10.19-slim
+
+# Set the working directory in the container
+WORKDIR /code
+
+# Copy requirements and install them
+COPY requirements.txt .
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Create a non-root user (Mandatory for Hugging Face Spaces)
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Move to the app directory and copy your code
+WORKDIR $HOME/app
+COPY --chown=user . .
+
+# Expose the default Hugging Face port
+EXPOSE 7860
+
+# Run FastAPI using uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List
+import uvicorn
+from fastapi.middleware.cors import CORSMiddleware
+import pickle
+import numpy as np
+
+with open('model.p', 'rb') as f:
+    model_dict = pickle.load(f)
+
+model = model_dict['model']
+label_encoder = model_dict['label_encoder']
+
+app = FastAPI(title="Sign Language Detection API")
+
+# Enable CORS so your React frontend (localhost:3000) can communicate with this API
+origins = [
+    # Your Vercel production URL (No trailing slash)
+    "https://sign-language-speller-43v971lbk-brians-projects-154a47fb.vercel.app",
+    
+    # Your local development URL
+    "http://localhost:3000",
+    "http://127.0.0.1:3000",
+]
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,  
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Define the structure for the landmark data
+class Landmark(BaseModel):
+    x: float
+    y: float
+    z: float
+
+class PredictionRequest(BaseModel):
+    landmarks: List[Landmark]
+def normalize_landmarks(landmarks):
+    """
+    Normalize landmarks by translating and scaling.
+    """
+    # Translate landmarks so that the wrist (landmark 0) is at the origin
+    wrist = landmarks[0]
+    normalized=[]
+    for landmark in landmarks:
+        normalized.append(landmark.x -wrist.x)
+        normalized.append(landmark.y -wrist.y)
+
+    return normalized
+
+@app.get("/")
+async def root():
+    return {"message": "Sign Language API is running"}
+
+@app.post("/predict")
+async def predict(request: PredictionRequest):
+    """
+    Receives 21 landmarks and returns the predicted alphabet.
+    """
+    try:
+        landmarks = request.landmarks
+        
+        # Ensure we have exactly 21 landmarks
+        if len(landmarks) != 21:
+            raise HTTPException(status_code=400, detail="Exactly 21 landmarks are required")
+
+        # --- MACHINE LEARNING INTEGRATION POINT ---
+        # This is where you would load your .joblib or .h5 model 
+        # and transform the landmarks into a feature vector.
+        # Example:
+        # data_point = []
+        # for lm in landmarks:
+        #     data_point.extend([lm.x, lm.y, lm.z])
+        model_input=np.array(normalize_landmarks(landmarks)).reshape(1,-1)
+        # print(model_input)
+        prediction = model.predict(model_input)
+
+        detected_char = label_encoder.inverse_transform(prediction)[0]
+        
+        # Mock logic for demonstration:
+        # Just returning 'A' to confirm the connection works
+        # detected_char = "A" 
+        
+        return {
+            "alphabet": detected_char,
+            "status": "success"
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    # Run the server on port 5000 to match your React fetch call
+    uvicorn.run(app, host="0.0.0.0", port=5000)

model.p

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2b41714cda0da454de806071ce01c0c7aae1018258bfe87ef943e170c7b2ae1b
+size 204763

requirements.txt

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+annotated-doc==0.0.4
+annotated-types==0.7.0
+anyio==4.12.0
+click==8.3.1
+colorama==0.4.6
+exceptiongroup==1.3.1
+fastapi==0.128.0
+h11==0.16.0
+idna==3.11
+joblib==1.5.3
+numpy==2.2.6
+pydantic==2.12.5
+pydantic_core==2.41.5
+scikit-learn==1.7.2
+scipy==1.15.3
+starlette==0.50.0
+threadpoolctl==3.6.0
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+uvicorn==0.40.0