Create app.py

app.py

@@ -0,0 +1,85 @@
+from flask import Flask, request, jsonify
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+app = Flask(__name__)
+
+print("🚀 Loading Dolphin-Phi-2 (uncensored)...")
+
+model_name = "cognitivecomputations/dolphin-2_6-phi-2"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    low_cpu_mem_usage=True,
+    trust_remote_code=True
+)
+
+print("✅ Model loaded!")
+
+@app.route('/v1/chat/completions', methods=['POST'])
+def generate():
+    try:
+        data = request.json
+        messages = data.get('messages', [])
+        max_tokens = data.get('max_tokens', 300)
+        temperature = data.get('temperature', 0.8)
+        
+        system_msg = ""
+        user_msg = ""
+        
+        for msg in messages:
+            if msg['role'] == 'system':
+                system_msg = msg['content']
+            elif msg['role'] == 'user':
+                user_msg = msg['content']
+        
+        prompt = f"<|im_start|>system\n{system_msg}<|im_end|>\n<|im_start|>user\n{user_msg}<|im_end|>\n<|im_start|>assistant\n"
+        
+        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+        
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=max_tokens,
+                temperature=temperature,
+                top_p=0.9,
+                do_sample=True,
+                pad_token_id=tokenizer.eos_token_id
+            )
+        
+        full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        response_text = full_response.split("<|im_start|>assistant")[-1].replace("<|im_end|>", "").strip()
+        
+        return jsonify({
+            "choices": [{
+                "message": {
+                    "role": "assistant",
+                    "content": response_text
+                }
+            }]
+        })
+        
+    except Exception as e:
+        print(f"❌ Error: {str(e)}")
+        return jsonify({"error": str(e)}), 500
+
+@app.route('/health', methods=['GET'])
+def health():
+    return jsonify({"status": "ok", "model": "dolphin-phi-2"})
+
+@app.route('/', methods=['GET'])
+def home():
+    return jsonify({
+        "message": "Uncensored LLM API",
+        "model": "dolphin-phi-2-2.7b",
+        "endpoints": {
+            "chat": "/v1/chat/completions (POST)",
+            "health": "/health (GET)"
+        }
+    })
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)