added /frame-details (#1)

- added /frame-details (8119b22648e35787a42fb20aaefe7ead6d118342)


Co-authored-by: Funke <[email protected]>

app.py

@@ -49,4 +49,55 @@ async def face_analyse(file: UploadFile = File(...)):
         return {"error": "Failed to make predictions."}
 
     # Assuming categories is a list of category labels
-    return dict(zip(categories, map(float, probs)))
+    return dict(zip(categories, map(float, probs)))
+
+    # Initialize the Meta-Llama-3-70B-Instruct pipeline
+llama_model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
+llama_pipeline = pipeline(
+    "text-generation",
+    model=llama_model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+@app.post("/frame-details")
+def frame_details(text: str):
+    """
+    Extract structured information from a given text about frames using the
+    Meta-Llama-3-70B-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
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)