Add Vapi Gemma API application

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application
+COPY app.py .
+
+# Expose port 7860 (Hugging Face Spaces default)
+EXPOSE 7860
+
+# Run with gunicorn
+CMD ["gunicorn", "app:app", "--bind", "0.0.0.0:7860", "--timeout", "300", "--workers", "1"]

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+import os
+
+app = Flask(__name__)
+CORS(app)
+
+# Model adları
+MODEL_NAME = "ilkeileri/gemma-sales-comprehensive"
+BASE_MODEL = "google/gemma-1.1-2b-it"
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+
+print("Loading base model...")
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    dtype=torch.float16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True
+)
+
+print("Loading LoRA adapters...")
+model = PeftModel.from_pretrained(base_model, MODEL_NAME)
+model.eval()
+
+print("Model loaded successfully!")
+
+@app.route("/", methods=["GET"])
+def health_check():
+    return jsonify({
+        "status": "ok",
+        "model": MODEL_NAME,
+        "base_model": BASE_MODEL
+    }), 200
+
+@app.route("/chat/completions", methods=["POST", "OPTIONS"])
+def chat_completions():
+    if request.method == "OPTIONS":
+        return "", 200
+    
+    try:
+        data = request.get_json()
+        messages = data.get("messages", [])
+        
+        if not messages:
+            prompt = data.get("prompt", "")
+        else:
+            last_message = messages[-1] if messages else {}
+            prompt = last_message.get("content", "")
+        
+        if not prompt:
+            return jsonify({"error": "No prompt provided"}), 400
+        
+        # Gemma formatında prompt
+        formatted_prompt = f"<start_of_turn>user\n{prompt}<end_of_turn>\n<start_of_turn>model\n"
+
+        # Model yanıtı üret
+        inputs = tokenizer(formatted_prompt, return_tensors="pt")
+        outputs = model.generate(
+            inputs["input_ids"], 
+            max_new_tokens=200, 
+            temperature=0.7,
+            do_sample=True,
+            top_p=0.9,
+            num_return_sequences=1
+        )
+
+        full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        response_text = full_response.replace(formatted_prompt, "").strip()
+        
+        # Vapi formatı
+        vapi_response = {
+            "choices": [{
+                "message": {
+                    "role": "assistant",
+                    "content": response_text
+                }
+            }]
+        }
+        
+        return jsonify(vapi_response), 200
+
+    except Exception as e:
+        print(f"Error: {str(e)}")
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)

requirements.txt

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+flask==3.0.0
+flask-cors==4.0.0
+gunicorn==21.2.0
+transformers>=4.38.0
+peft>=0.8.0
+torch>=2.0.0
+accelerate>=0.25.0
+sentencepiece>=0.1.99
+protobuf>=3.20.0