Delete app.py

app.py

@@ -1,138 +0,0 @@
-from langchain.prompts import PromptTemplate
-from langchain_huggingface import HuggingFaceEndpoint
-from PIL import Image
-import os
-import secrets
-from pathlib import Path
-import tempfile
-import gradio as gr
-
-# Initialize the Hugging Face BLIP model
-image_captioning_model = HuggingFaceEndpoint(
-    endpoint_url="https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-base",
-    huggingfacehub_api_token=os.getenv("HUGGING_FACE_API"),  # Ensure you set this in your environment
-    temperature=0.7,
-    max_new_tokens=1024,
-)
-math_llm=HuggingFaceEndpoint(
-    endpoint_url="https://api-inference.huggingface.co/models/Qwen/Qwen2.5-Math-7B-Instruct",
-    huggingfacehub_api_token=os.getenv("HUGGING_FACE_API"),  # Ensure you set this in your environment
-    temperature=0.7,
-    max_new_tokens=1024,)
-# Function to process the image
-def process_image(image, shouldConvert=False):
-    # Ensure temporary directory exists
-    uploaded_file_dir = os.environ.get("GRADIO_TEMP_DIR") or str(
-        Path(tempfile.gettempdir()) / "gradio"
-    )
-    os.makedirs(uploaded_file_dir, exist_ok=True)
-    
-    # Save the uploaded image
-    name = f"tmp{secrets.token_hex(20)}.jpg"
-    filename = os.path.join(uploaded_file_dir, name)
-    if shouldConvert:
-        # Convert image to RGB mode if it contains transparency
-        new_img = Image.new("RGB", size=(image.width, image.height), color=(255, 255, 255))
-        new_img.paste(image, (0, 0), mask=image)
-        image = new_img
-    image.save(filename)
-
-    # Define a PromptTemplate for text instruction
-    template = """
-    You are a helpful AI assistant.
-    Please describe the math-related content in this image, ensuring that any LaTeX formulas are correctly transcribed. 
-    Non-mathematical details do not need to be described.
-
-    Image Path: {image}
-    """
-    prompt_template = PromptTemplate(
-        input_variables=["image"],  # Dynamically insert the image path
-        template=template
-    )
-
-    # Create the text instruction by rendering the prompt template
-    prompt = prompt_template.format(image=f"file://{filename}")
-
-    # Use the model with both the image and the generated prompt
-    with open(filename, "rb") as img_file:
-        response = image_captioning_model({
-            "inputs": {
-                "image": img_file,
-                "text": prompt
-            }
-        })
-
-    # Return the model's response
-    return response
-
-def get_math_response(image_description, user_question):
-    template = """
-    You are a helpful AI assistant specialized in solving math reasoning problems. 
-    Analyze the following question carefully and provide a step-by-step explanation along with the answer.
-    Image description : {image_description}
-    Question: {user_question}?
-    """
-
-    prompt_template = PromptTemplate(
-        input_variables=["user_question","image_description"],  # Define the placeholder(s) in the template
-        template=template
-    )
-    formatted_prompt = prompt_template.format(user_question=user_question, image_description=image_description)
-
-    # Pass the formatted prompt to the model
-    response = math_llm(formatted_prompt)
-
-    # Print the response
-    yield response
-
-def math_chat_bot(image, sketchpad, question, state):
-    current_tab_index = state["tab_index"]
-    image_description = None
-    # Upload
-    if current_tab_index == 0:
-        if image is not None:
-            image_description = process_image(image)
-    # Sketch
-    elif current_tab_index == 1:
-        print(sketchpad)
-        if sketchpad and sketchpad["composite"]:
-            image_description = process_image(sketchpad["composite"], True)
-    yield from get_math_response(image_description, question)
-css = """
-#qwen-md .katex-display { display: inline; }
-#qwen-md .katex-display>.katex { display: inline; }
-#qwen-md .katex-display>.katex>.katex-html { display: inline; }
-"""
-
-def tabs_select(e: gr.SelectData, _state):
-    _state["tab_index"] = e.index
-    return _state
-
-with gr.Blocks(css=css) as demo:
-    state = gr.State({"tab_index": 0})
-
-    with gr.Row():
-        with gr.Column():
-            with gr.Tabs() as input_tabs:
-                with gr.Tab("Upload"):
-                    input_image = gr.Image(type="pil", label="Upload")
-                with gr.Tab("Sketch"):
-                    input_sketchpad = gr.Sketchpad(type="pil", label="Sketch", layers=False)
-            
-            input_tabs.select(fn=tabs_select, inputs=[state], outputs=[state])
-
-            input_text = gr.Textbox(label="Input your question")
-            with gr.Row():
-                clear_btn = gr.ClearButton([input_image, input_sketchpad, input_text])
-                submit_btn = gr.Button("Submit", variant="primary")
-
-        with gr.Column():
-            output_md = gr.Markdown(label="Answer", elem_id="qwen-md")
-
-    submit_btn.click(
-        fn=math_chat_bot,
-        inputs=[input_image, input_sketchpad, input_text, state],
-        outputs=output_md,
-    )
-
-demo.launch()