Create main.py

main.py

@@ -0,0 +1,133 @@
+import os  # Import the os module for working with the operating system
+from fastapi import FastAPI, HTTPException  # Import necessary modules from FastAPI
+from pydantic import BaseModel  # Import BaseModel from pydantic for data validation
+from huggingface_hub import InferenceClient  # Import InferenceClient from huggingface_hub
+import uvicorn  # Import uvicorn for running the FastAPI application
+
+app = FastAPI()  # Create a FastAPI instance
+
+# Define the primary and fallback models
+primary = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+fallbacks = ["mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mistral-7B-Instruct-v0.1"]
+
+# Define the data model for the request body
+class Item(BaseModel):
+    input: str = None
+    system_prompt: str = None
+    system_output: str = None
+    history: list = None
+    templates: list = None
+    temperature: float = 0.0
+    max_new_tokens: int = 1048
+    top_p: float = 0.15
+    repetition_penalty: float = 1.0
+    key: str = None
+
+# Function to generate the response JSON
+def generate_response_json(item, output, tokens, model_name):
+    return {
+        "settings": {
+            "input": item.input if item.input is not None else "",
+            "system prompt": item.system_prompt if item.system_prompt is not None else "",
+            "system output": item.system_output if item.system_output is not None else "",
+            "temperature": f"{item.temperature}" if item.temperature is not None else "",
+            "max new tokens": f"{item.max_new_tokens}" if item.max_new_tokens is not None else "",
+            "top p": f"{item.top_p}" if item.top_p is not None else "",
+            "repetition penalty": f"{item.repetition_penalty}" if item.repetition_penalty is not None else "",
+            "do sample": "True",
+            "seed": "42"
+        },
+        "response": {
+            "output": output.strip().lstrip('\n').rstrip('\n').lstrip('<s>').rstrip('</s>').strip(),
+            "unstripped": output,
+            "tokens": tokens,
+            "model": "primary" if model_name == primary else "fallback",
+            "name": model_name
+        }
+    }
+
+# Endpoint for generating text
+@app.post("/")
+async def generate_text(item: Item = None):
+    try:
+        if item is None:
+            raise HTTPException(status_code=400, detail="JSON body is required.")
+
+        if item.input is None and item.system_prompt is None or item.input == "" and item.system_prompt == "":
+            raise HTTPException(status_code=400, detail="Parameter `input` or `system prompt` is required.")
+        
+        input_ = ""
+        if item.system_prompt != None and item.system_output != None:
+            input_ = f"<s>[INST] {item.system_prompt} [/INST] {item.system_output}</s>"
+        elif item.system_prompt != None:
+            input_ = f"<s>[INST] {item.system_prompt} [/INST]</s>"
+        elif item.system_output != None:
+            input_ = f"<s>{item.system_output}</s>"
+
+        if item.templates != None:
+            for num, template in enumerate(item.templates, start=1):
+                input_ += f"\n<s>[INST] Beginning of archived conversation {num} [/INST]</s>"
+                for i in range(0, len(template), 2):
+                    input_ += f"\n<s>[INST] {template[i]} [/INST]"
+                    input_ += f"\n{template[i + 1]}</s>"
+                input_ += f"\n<s>[INST] End of archived conversation {num} [/INST]</s>"
+
+        input_ += f"\n<s>[INST] Beginning of active conversation [/INST]</s>"
+        if item.history != None:
+            for input_, output_ in item.history:
+                input_ += f"\n<s>[INST] {input_} [/INST]"
+                input_ += f"\n{output_}"
+        input_ += f"\n<s>[INST] {item.input} [/INST]"
+
+        temperature = float(item.temperature)
+        if temperature < 1e-2:
+            temperature = 1e-2
+        top_p = float(item.top_p)
+
+        generate_kwargs = dict(
+            temperature=temperature,
+            max_new_tokens=item.max_new_tokens,
+            top_p=top_p,
+            repetition_penalty=item.repetition_penalty,
+            do_sample=True,
+            seed=42,
+        )
+
+        tokens = 0
+        client = InferenceClient(primary)
+        stream = client.text_generation(input_, **generate_kwargs, stream=True, details=True, return_full_text=True)
+        output = ""
+        for response in stream:
+            tokens += 1
+            output += response.token.text
+        return generate_response_json(item, output, tokens, primary)
+
+    except HTTPException as http_error:
+        raise http_error
+
+    except Exception as e:
+        tokens = 0
+        error = ""
+        
+        for model in fallbacks:
+            try:
+                client = InferenceClient(model)
+                stream = client.text_generation(input_, **generate_kwargs, stream=True, details=True, return_full_text=True)
+                output = ""
+                for response in stream:
+                    tokens += 1
+                    output += response.token.text
+                return generate_response_json(item, output, tokens, model)
+
+            except Exception as e:
+                error = f"All models failed. {e}" if e else "All models failed."
+                continue
+
+        raise HTTPException(status_code=500, detail=error)
+
+    if "KEY" in os.environ:
+        if item.key != os.environ["KEY"]:
+            raise HTTPException(status_code=401, detail="Valid key is required.")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)