Upload folder using huggingface_hub

Dockerfile

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+FROM python:3.9
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy requirements and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code
+COPY . .
+
+# Set permissions for the HF user
+RUN chmod -R 777 /app
+
+# Hugging Face Spaces requires port 7860
+EXPOSE 7860
+
+# Run the application using Gunicorn for stability
+# 'app:app' refers to the Flask object in your app.py file
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]

app.py

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+from flask import Flask, render_template, request, jsonify
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+app = Flask(__name__)
+
+# Load your specific SciBERT model from HF
+MODEL_PATH = "gsstec/aegis-scibert-technical"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.json
+    year = data.get("year", "2026")
+    
+    # Construct technical context for SciBERT
+    input_text = f"Scientific and technological advancements emergent in the year {year}."
+    
+    # Tokenization
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512)
+    
+    # Prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        # Assuming classification for tech maturity/risk
+        prediction = torch.softmax(outputs.logits, dim=1).tolist()[0]
+
+    # This result would then be sent to the Conductor/LangGraph for Econ processing
+    return jsonify({
+        "year": year,
+        "tech_maturity_score": prediction[0], 
+        "status": "SENT_TO_CONDUCTOR"
+    })
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)

deploy_fixed.py

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+#!/usr/bin/env python3
+"""
+Fixed deployment script for TEC application to Hugging Face Spaces
+"""
+
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+def run_command(cmd, cwd=None):
+    """Run a command and return the result"""
+    try:
+        result = subprocess.run(cmd, shell=True, cwd=cwd, capture_output=True, text=True)
+        if result.returncode != 0:
+            print(f"Error running command: {cmd}")
+            print(f"Error output: {result.stderr}")
+            return False
+        print(f"Success: {cmd}")
+        if result.stdout:
+            print(result.stdout)
+        return True
+    except Exception as e:
+        print(f"Exception running command {cmd}: {e}")
+        return False
+
+def check_files():
+    """Check if required files exist"""
+    required_files = ['app.py', 'requirements.txt', 'Dockerfile']
+    missing_files = []
+    
+    for file in required_files:
+        if not Path(file).exists():
+            missing_files.append(file)
+    
+    if missing_files:
+        print(f"Missing required files: {missing_files}")
+        return False
+    
+    print("All required files found")
+    return True
+
+def get_hf_username(token):
+    """Get the Hugging Face username from the token"""
+    try:
+        from huggingface_hub import HfApi
+        api = HfApi(token=token)
+        user_info = api.whoami()
+        return user_info['name']
+    except Exception as e:
+        print(f"Could not get username: {e}")
+        return None
+
+def deploy_to_hf():
+    """Deploy to Hugging Face Spaces"""
+    if not check_files():
+        return False
+    
+    # Check if we have HF token
+    hf_token = os.getenv('HF_TOKEN')
+    if not hf_token:
+        print("HF_TOKEN environment variable not set")
+        print("Please run: deploy_with_token.bat")
+        print("Or manually set: set HF_TOKEN=your_token_here")
+        return False
+    
+    print("Starting deployment to Hugging Face Spaces...")
+    
+    # Install huggingface_hub if not available
+    print("Installing huggingface_hub...")
+    if not run_command("pip install huggingface_hub"):
+        return False
+    
+    # Get username
+    print("Getting your Hugging Face username...")
+    username = get_hf_username(hf_token)
+    if not username:
+        username = input("Enter your HF username: ").strip()
+        if not username:
+            print("Error: Username is required")
+            return False
+    else:
+        print(f"Found username: {username}")
+    
+    # Get space name
+    space_name = input(f"Enter space name (default: tec-app): ").strip()
+    if not space_name:
+        space_name = "tec-app"
+    
+    repo_id = f"{username}/{space_name}"
+    print(f"Will create space: {repo_id}")
+    
+    # Create deployment script
+    deploy_script = f'''
+from huggingface_hub import HfApi
+import os
+
+api = HfApi(token="{hf_token}")
+repo_id = "{repo_id}"
+
+try:
+    print(f"Creating space: {{repo_id}}")
+    
+    # Create the space first
+    api.create_repo(
+        repo_id=repo_id,
+        repo_type="space",
+        space_sdk="docker",
+        exist_ok=True,
+        private=False
+    )
+    
+    print("Space created successfully!")
+    print("Uploading files...")
+    
+    # Upload files
+    api.upload_folder(
+        folder_path=".",
+        repo_id=repo_id,
+        repo_type="space",
+        ignore_patterns=[".git", "__pycache__", "*.pyc", "temp_deploy.py", "deploy_fixed.py"]
+    )
+    
+    print(f"Successfully deployed to https://huggingface.co/spaces/{{repo_id}}")
+    print("Your app will be available in a few minutes!")
+    print("Note: It may take 2-3 minutes for the Docker container to build and start.")
+    
+except Exception as e:
+    print(f"Deployment failed: {{e}}")
+    import traceback
+    traceback.print_exc()
+'''
+    
+    with open('temp_deploy.py', 'w') as f:
+        f.write(deploy_script)
+    
+    success = run_command("python temp_deploy.py")
+    
+    # Cleanup
+    if Path('temp_deploy.py').exists():
+        Path('temp_deploy.py').unlink()
+    
+    return success
+
+if __name__ == "__main__":
+    print("TEC Application Deployment Script (Fixed)")
+    print("=" * 45)
+    
+    if deploy_to_hf():
+        print("Deployment completed successfully!")
+    else:
+        print("Deployment failed!")
+        sys.exit(1)

requirements.txt

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+flask
+transformers
+torch
+sentencepiece
+gunicorn

templates/index.html

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+<!DOCTYPE html>
+<html>
+<head>
+    <title>Aegis-10 | Tech Entry</title>
+    <style>
+        body { font-family: sans-serif; background: #0b0e14; color: #00ffcc; display: flex; justify-content: center; align-items: center; height: 100vh; }
+        .container { border: 1px solid #00ffcc; padding: 20px; border-radius: 8px; text-align: center; }
+        input { background: transparent; border: 1px solid #00ffcc; color: white; padding: 10px; margin-bottom: 10px; }
+        button { background: #00ffcc; color: #0b0e14; border: none; padding: 10px 20px; cursor: pointer; font-weight: bold; }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h2>SOVEREIGN TECH ENTRY</h2>
+        <input type="number" id="year" placeholder="Enter Year (e.g. 2026)" value="2026">
+        <br>
+        <button onclick="runTechModal()">START PREDICTION</button>
+        <div id="results" style="margin-top: 20px;"></div>
+    </div>
+
+    <script>
+        async function runTechModal() {
+            const year = document.getElementById('year').value;
+            const res = await fetch('/predict', {
+                method: 'POST',
+                headers: {'Content-Type': 'application/json'},
+                body: JSON.stringify({year: year})
+            });
+            const data = await res.json();
+            document.getElementById('results').innerText = `Tech Score: ${data.tech_maturity_score.toFixed(4)} | Status: ${data.status}`;
+        }
+    </script>
+</body>
+</html>