Llama deleted

app.py

@@ -53,51 +53,3 @@ async def face_analyse(file: UploadFile = File(...)):
     # Assuming categories is a list of category labels
     return dict(zip(categories, map(float, probs)))
 
-    # Initialize the Meta-Llama-3-8B-Instruct pipeline
-llama_model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
-llama_pipeline = pipeline(
-    "text-generation",
-    model=llama_model_id,
-    model_kwargs={"torch_dtype": torch.bfloat16},
-    device_map="auto",
-    auth_token=access_token
-)
-
-
-@app.post("/frame-details")
-def frame_details(text: str):
-    
-    # Extract structured information from a given text about frames using the Meta-Llama-3-8B-Instruct model. The model will output details like price, color, etc.
-    
-    messages = [
-        {"role": "system", "content": "You are an api chatbot for frames and glasses who always responds with only a json. Extract the infomation given into a structured json for frame details"},
-        {"role": "user", "content": text},
-    ]
-
-    terminators = [
-        llama_pipeline.tokenizer.eos_token_id,
-        llama_pipeline.tokenizer.convert_tokens_to_ids("")
-    ]
-
-    outputs = llama_pipeline(
-        messages,
-        max_new_tokens=256,
-        eos_token_id=terminators,
-        do_sample=True,
-        temperature=0.6,
-        top_p=0.9,
-    )
-    
-    # Extract the last generated text from the output
-    generated_text = outputs[0]["generated_text"]
-    
-    # Parse the generated text to extract structured information (this is an example and should be customized)
-    # Here, you would add your own logic to parse the generated text
-    # For now, we'll assume the generated text is in JSON format
-    try:
-        extracted_info = eval(generated_text)  # It's recommended to use `json.loads` in a real application
-    except Exception as e:
-        return {"error": "Failed to parse the generated text."}
-
-    return extracted_info
-