Nechama commited on
Commit
454e48d
·
verified ·
1 Parent(s): 64d297a

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +38 -59
app.py CHANGED
@@ -1,63 +1,42 @@
1
  import gradio as gr
2
- from huggingface_hub import InferenceClient
3
-
4
- """
5
- For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
6
- """
7
- client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
8
-
9
-
10
- def respond(
11
- message,
12
- history: list[tuple[str, str]],
13
- system_message,
14
- max_tokens,
15
- temperature,
16
- top_p,
17
- ):
18
- messages = [{"role": "system", "content": system_message}]
19
-
20
- for val in history:
21
- if val[0]:
22
- messages.append({"role": "user", "content": val[0]})
23
- if val[1]:
24
- messages.append({"role": "assistant", "content": val[1]})
25
-
26
- messages.append({"role": "user", "content": message})
27
-
28
- response = ""
29
-
30
- for message in client.chat_completion(
31
- messages,
32
- max_tokens=max_tokens,
33
- stream=True,
34
- temperature=temperature,
35
- top_p=top_p,
36
- ):
37
- token = message.choices[0].delta.content
38
-
39
- response += token
40
- yield response
41
-
42
- """
43
- For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
44
- """
45
- demo = gr.ChatInterface(
46
- respond,
47
- additional_inputs=[
48
- gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
49
- gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
50
- gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
51
- gr.Slider(
52
- minimum=0.1,
53
- maximum=1.0,
54
- value=0.95,
55
- step=0.05,
56
- label="Top-p (nucleus sampling)",
57
- ),
58
- ],
59
  )
60
 
61
-
62
  if __name__ == "__main__":
63
- demo.launch()
 
1
  import gradio as gr
2
+ from PIL import Image
3
+ from transformers import pipeline, AutoModelForVision2Seq, AutoProcessor
4
+ import torch
5
+
6
+ # Load the OpenGVLab/InternVL-Chat-V1-5 model and processor
7
+ processor = AutoProcessor.from_pretrained("OpenGVLab/InternVL-Chat-V1-5")
8
+ model = AutoModelForVision2Seq.from_pretrained("OpenGVLab/InternVL-Chat-V1-5")
9
+
10
+ # Load the Llama3 model for text processing
11
+ llama_model = pipeline("text2text-generation", model="llama3")
12
+
13
+ def process_image(image):
14
+ # Process the image to extract the recipe using OpenGVLab
15
+ inputs = processor(images=image, return_tensors="pt")
16
+ generated_ids = model.generate(**inputs)
17
+ extracted_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
18
+
19
+ return extracted_text
20
+
21
+ def adjust_recipe(extracted_text, adjustment):
22
+ # Create the prompt for Llama3 to adjust the recipe
23
+ prompt = f"Here is a recipe: {extracted_text}. Please {adjustment} the recipe."
24
+ response = llama_model(prompt)
25
+ return response[0]['generated_text']
26
+
27
+ def app(image, adjustment):
28
+ extracted_text = process_image(image)
29
+ adjusted_recipe = adjust_recipe(extracted_text, adjustment)
30
+ return adjusted_recipe
31
+
32
+ # Create the Gradio interface
33
+ interface = gr.Interface(
34
+ fn=app,
35
+ inputs=[gr.inputs.Image(type="pil"), gr.inputs.Dropdown(["double", "halve"])],
36
+ outputs="text",
37
+ title="Recipe Adjuster",
38
+ description="Upload an image of a recipe, and this app will double or halve the recipe."
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
39
  )
40
 
 
41
  if __name__ == "__main__":
42
+ interface.launch()