Create app.py

app.py

@@ -0,0 +1,82 @@
+
+
+
+import uvicorn
+import json
+import requests
+from flask import Flask, request, jsonify
+
+
+
+app = Flask(__name__)
+
+rq = requests.sessions.Session()
+
+model_names = [
+        "meta-llama/Meta-Llama-3-70B-Instruct",
+        "meta-llama/Meta-Llama-3-8B-Instruct",
+        "mistralai/Mixtral-8x22B-Instruct-v0.1",
+        "mistralai/Mixtral-8x22B-v0.1",
+        "microsoft/WizardLM-2-8x22B",
+        "microsoft/WizardLM-2-7B",
+        "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
+        "google/gemma-1.1-7b-it",
+        "databricks/dbrx-instruct",
+        "mistralai/Mixtral-8x7B-Instruct-v0.1",
+        "mistralai/Mistral-7B-Instruct-v0.2",
+        "meta-llama/Llama-2-70b-chat-hf",
+        "cognitivecomputations/dolphin-2.6-mixtral-8x7b",
+        "codellama/CodeLlama-70b-Instruct-hf"
+    ]
+
+
+
+
+def DeepinFra(Api:str, messages:list ,model:str, max_tokens: int = 512, temperature: float = 0.7):
+
+    url = "https://api.deepinfra.com/v1/openai/chat/completions"
+    headers ={
+        "Authorization" : f"Bearer {Api}"
+    }
+
+
+
+
+    data = json.dumps(
+        {
+            'model': model,
+            'messages': messages,
+            'temperature': temperature,
+            'max_tokens': max_tokens,
+            'stop': [],
+            'stream': False
+        }, separators=(',', ':')
+    )
+    
+    try:
+        result = rq.post(url=url, headers=headers, data=data)
+        
+        return result.json()['choices'][0]['message']['content']
+    except:
+
+        
+        return "Response content: " + result.text
+
+
+
+
+@app.route("/generate-text-deep", methods=["POST"])
+def generate_text():
+    data = request.json
+    message = data.get("message")
+    Api = data.get("api_key")
+    model_name = data.get("model_name", "mistralai/Mixtral-8x22B-Instruct-v0.1")
+    max_tokens = data.get("max_tokens", 512)
+    temperature = data.get("temperature", 0.7)
+
+    if not message or not Api:
+        return jsonify({"error": "Missing required fields"}), 400
+
+    response = DeepinFra(Api=Api , messages=message, model=model_name, max_tokens=max_tokens, temperature=temperature)
+
+    return jsonify({"response": response}), 200