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copy.py

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+from fastapi import FastAPI
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from huggingface_hub import InferenceClient
+import uvicorn
+from typing import Generator
+import json  # Asegúrate de que esta línea esté al principio del archivo
+import nltk
+import os
+
+
+nltk.data.path.append(os.getenv('NLTK_DATA'))
+
+app = FastAPI()
+
+# Initialize the InferenceClient with your model
+client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
+
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+
+
+class Item(BaseModel):
+    prompt: str
+    history: list
+    system_prompt: str
+    temperature: float = 0.8
+    max_new_tokens: int = 12000
+    top_p: float = 0.15
+    repetition_penalty: float = 1.0
+
+def format_prompt(current_prompt, history):
+    formatted_history = "<s>"
+    for entry in history:
+        if entry["role"] == "user":
+            formatted_history += f"[USER] {entry['content']} [/USER]"
+        elif entry["role"] == "assistant":
+            formatted_history += f"[ASSISTANT] {entry['content']} [/ASSISTANT]"
+    formatted_history += f"[USER] {current_prompt} [/USER]</s>"
+    return formatted_history
+
+
+def generate_stream(item: Item) -> Generator[bytes, None, None]:
+    formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
+    # Estimate token count for the formatted_prompt
+    input_token_count = len(nltk.word_tokenize(formatted_prompt))  # NLTK tokenization
+
+    # Ensure total token count doesn't exceed the maximum limit
+    max_tokens_allowed = 32768
+    max_new_tokens_adjusted = max(1, min(item.max_new_tokens, max_tokens_allowed - input_token_count))
+
+    generate_kwargs = {
+        "temperature": item.temperature,
+        "max_new_tokens": max_new_tokens_adjusted,
+        "top_p": item.top_p,
+        "repetition_penalty": item.repetition_penalty,
+        "do_sample": True,
+        "seed": 42,
+    }
+
+    # Stream the response from the InferenceClient
+    for response in client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True):
+        # This assumes 'details=True' gives you a structure where you can access the text like this
+        chunk = {
+            "text": response.token.text,
+            "complete": response.generated_text is not None  # Adjust based on how you detect completion
+        }
+        yield json.dumps(chunk).encode("utf-8") + b"\n"
+
+@app.post("/generate/")
+async def generate_text(item: Item):
+    # Stream response back to the client
+    return StreamingResponse(generate_stream(item), media_type="application/x-ndjson")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)