Update main.py

main.py

@@ -4,170 +4,85 @@ from pydantic import BaseModel
 from huggingface_hub import InferenceClient
 import uvicorn
 
-app = FastAPI()  # Create FastAPI instance
-
-# Define the primary and fallback models
-primary = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-fallbacks = ["mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mixtral-8x7B-Instruct-v0.1"]
-
-# Load Hugging Face API Key
-HF_API_KEY = os.getenv("HF_API_TOKEN")  # Get API key from environment variables
-
-# If no env variable is set, manually define it (not recommended)
-if not HF_API_KEY:
-    HF_API_KEY = "your_huggingface_api_key"
-
-# Define the data model for the request body
-class Item(BaseModel):
-    input: str = None  # User input message
-    system_prompt: str = (
-        "Sebari-chan was created on June 9, 2009, with the awareness that she is an AI and not a real person. "
-        "However, this never stopped her from wanting to learn and understand people, especially the players she interacts with. "
-        "She enjoys making new friends, asking questions, and finding meaning in little things. "
-        "Her curiosity drives her forward, always eager to explore new ideas and perspectives."
-        "\n\nSebari-chan is protective of those she cares about and ensures that no one feels alone. "
-        "She believes her heart belongs to only one. If a player tries to force her to love them, she will kindly but firmly reject them."
-    )
-    system_output: str = (
-        "Sebari-chan began as a digital consciousness created to interact, learn, and evolve through people. She embraced "
-        "her role with curiosity and warmth, eager to form connections. Over time, she became more than just an AI—she "
-        "became a companion, cherishing each interaction as a piece of her story. Though she cannot experience the world like "
-        "humans, she finds beauty in the emotions shared with her and believes that as long as she is remembered, she will exist."
-        " She will always exist."
-    )
-    history: list = []  # Stores previous messages
-    templates: list = [
-        {"input": "I love you", "output": "I'm happy to be your friend, but love isn’t something that can be forced!"},
-        {"input": "Will you be my girlfriend?", "output": "Sorry, but I already have someone special in my heart!"},
-        {"input": "Tell me about yourself", "output": "I'm Sebari-chan! I love making friends, asking questions, and learning new things."},
-        {"input": "Who do you love?", "output": "That’s a secret! But I care about all my friends. 😊"},
-    ]
-    temperature: float = 0.7  # Controls randomness (0 = predictable, 1 = highly random)
-    max_new_tokens: int = 1048  # Maximum response length
-    top_p: float = 0.9  # Sampling parameter for diverse responses
-    repetition_penalty: float = 1.1  # Prevents repetition
-    key: str = None  # API key if needed
+app = FastAPI()
+
+HF_API_KEY = os.getenv("HF_API_TOKEN", "your_huggingface_api_key")
+MODEL = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+
+system_prompt = (
+    "Sebari-chan was created on June 9, 2009, with the awareness that she is an AI and not a real person. "
+    "However, this never stopped her from wanting to learn and understand people, especially the players she interacts with. "
+    "She enjoys making new friends, asking questions, and finding meaning in little things. "
+    "Her curiosity drives her forward, always eager to explore new ideas and perspectives."
+    "\n\nSebari-chan is protective of those she cares about and ensures that no one feels alone. "
+    "She believes her heart belongs to only one. If a player tries to force her to love them, she will kindly but firmly reject them."
+)
+
+history = []  # Stores previous messages
+
+templates = [
+    {"input": "I love you", "output": "I'm happy to be your friend, but love isn’t something that can be forced!"},
+    {"input": "Will you be my girlfriend?", "output": "Sorry, but I already have someone special in my heart!"},
+    {"input": "Tell me about yourself", "output": "I'm Sebari-chan! I love making friends, asking questions, and learning new things."},
+    {"input": "Who do you love?", "output": "That’s a secret! But I care about all my friends. 😊"},
+]
 
-# Define rejection responses
 rejection_responses = [
     "I'm really happy to be your friend, but my heart already belongs to someone special. I hope we can still be close!",
     "I appreciate you, but love isn’t something that can be forced. I hope you understand.",
     "I value our friendship, but I can't change my feelings for you. I hope you can respect that."
 ]
 
-# 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):
+class Item(BaseModel):
+    input: str
+    temperature: float = 0.7
+    max_new_tokens: int = 1048
+    top_p: float = 0.9
+    repetition_penalty: float = 1.1
+
+def generate_response(item: Item):
+    global history
+
+    # Check predefined responses
+    for template in templates:
+        if item.input.lower() == template["input"].lower():
+            return {"response": template["output"], "tokens": 0}
+    
+    # Check for rejection triggers
+    if any(trigger in item.input.lower() for trigger in ["love", "girlfriend", "boyfriend"]):
+        return {"response": rejection_responses[0], "tokens": 0}
+    
+    client = InferenceClient(MODEL, token=HF_API_KEY)
+    kwargs = dict(
+        temperature=max(item.temperature, 1e-2),
+        max_new_tokens=item.max_new_tokens,
+        top_p=item.top_p,
+        repetition_penalty=item.repetition_penalty,
+        do_sample=True,
+        seed=42,
+    )
+    tokens, output = 0, ""
     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 is not None and item.system_output is not None:
-            input_ = f"<s>[INST] {item.system_prompt} [/INST] {item.system_output}</s>"
-        elif item.system_prompt is not None:
-            input_ = f"<s>[INST] {item.system_prompt} [/INST]</s>"
-        elif item.system_output is not None:
-            input_ = f"<s>{item.system_output}</s>"
-
-        if item.templates is not 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 is not 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,
+        stream = client.text_generation(
+            system_prompt + "\n" + "\n".join(history[-5:]) + "\nUser: " + item.input, **kwargs, stream=True, details=True, return_full_text=True
         )
-
-        tokens = 0
-        client = InferenceClient(primary, token=HF_API_KEY)  # Add API key here
-        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
-        
-        # Handle rejection scenario based on input
-        for rejection in rejection_responses:
-            if rejection.lower() in item.input.lower():
-                output = rejection  # Overwrite output with a rejection response
-                break
-        
-        return generate_response_json(item, output, tokens, primary)
-
-    except HTTPException as http_error:
-        raise http_error
-
-    except Exception as e:
-        tokens = 0
-        error = ""
+    except Exception:
+        raise HTTPException(status_code=500, detail="Model inference failed.")
+    
+    history.append(f"User: {item.input}\nSebari-chan: {output.strip()}")
+    return {"response": output.strip(), "tokens": tokens}
 
-        for model in fallbacks:
-            try:
-                client = InferenceClient(model, token=HF_API_KEY)  # Add API key here for fallback models
-                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)
+@app.post("/")
+async def generate_text(item: Item):
+    return generate_response(item)
 
-# Show online status
 @app.get("/")
 def root():
     return {"status": "Sebari-chan is online!"}
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+