Update main.py

main.py

@@ -3,44 +3,43 @@ from pydantic import BaseModel
 from huggingface_hub import InferenceClient
 import uvicorn
 
-
 app = FastAPI()
 
-client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+# Initialize the InferenceClient with the specified model
+client = InferenceClient("nvidia/Llama-3.1-Nemotron-70B-Instruct-HF")
 
-class Item(BaseModel):
-    prompt: str
-    history: list
-    system_prompt: str
+# Define the structure of the request body
+class CourseRequest(BaseModel):
+    course_name: str
+    history: list = []  # Keeping history optional
     temperature: float = 0.0
     max_new_tokens: int = 1048
     top_p: float = 0.15
     repetition_penalty: float = 1.0
 
-def format_prompt(message, history):
+# Format the prompt for the model
+def format_prompt(course_name, history):
     prompt = "<s>"
     for user_prompt, bot_response in history:
-        prompt += f"[INST] {user_prompt} [/INST]"
-        prompt += f" {bot_response}</s> "
-    prompt += f"[INST] {message} [/INST]"
+        prompt += f"[INST] {user_prompt} [/INST] {bot_response} </s> "
+    prompt += f"[INST] Generate a roadmap for the course: {course_name} [/INST]"
     return prompt
 
-def generate(item: Item):
-    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,
-    )
-
-    formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
+# Generate text using the specified parameters
+def generate(course_request: CourseRequest):
+    temperature = max(float(course_request.temperature), 1e-2)
+    top_p = float(course_request.top_p)
+
+    generate_kwargs = {
+        'temperature': temperature,
+        'max_new_tokens': course_request.max_new_tokens,
+        'top_p': top_p,
+        'repetition_penalty': course_request.repetition_penalty,
+        'do_sample': True,
+        'seed': 42,
+    }
+
+    formatted_prompt = format_prompt(course_request.course_name, course_request.history)
     stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
     output = ""
 
@@ -48,7 +47,11 @@ def generate(item: Item):
         output += response.token.text
     return output
 
-@app.post("/generate/")
-async def generate_text(item: Item):
-    return {"response": generate(item)}
+# Define the API endpoint for generating course roadmaps
+@app.post("/generate-roadmap/")
+async def generate_roadmap(course_request: CourseRequest):
+    return {"roadmap": generate(course_request)}
 
+# Run the application (uncomment the next two lines if running this as a standalone script)
+# if __name__ == "__main__":
+#     uvicorn.run(app, host="0.0.0.0", port=8000)