Update main.py

main.py

@@ -1,57 +1,39 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
 from huggingface_hub import InferenceClient
-import uvicorn
 
 app = FastAPI()
 
-# Initialize the InferenceClient with the specified model
+# Initialize the inference client for the AI model
 client = InferenceClient("nvidia/Llama-3.1-Nemotron-70B-Instruct-HF")
 
-# 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
-
-# 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] {bot_response} </s> "
-    prompt += f"[INST] Generate a roadmap for the course: {course_name} [/INST]"
-    return prompt
-
-# 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)
 
+def format_prompt(course_name: str):
+    return f"Please generate a detailed roadmap for the course '{course_name}'. Include key topics, recommended resources, and a learning timeline."
+
+def generate_roadmap(item: CourseRequest):
+    prompt = format_prompt(item.course_name)
+
+    # You can adjust these parameters as needed
     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,
+        "temperature": 0.7,
+        "max_new_tokens": 150,
+        "top_p": 0.9,
+        "repetition_penalty": 1.1,
     }
 
-    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)
+    # Call the model to generate the roadmap
+    stream = client.text_generation(prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
     output = ""
 
     for response in stream:
         output += response.token.text
+    
     return output
 
-# 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)
+@app.post("/generate/")
+async def generate_roadmap_endpoint(course_request: CourseRequest):
+    roadmap = generate_roadmap(course_request)
+    return {"roadmap": roadmap}