commit gradio app

app.py

@@ -0,0 +1,406 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[17]:
+
+
+import pickle
+from PIL import Image
+import numpy as np
+import gradio as gr
+from pathlib import Path
+from transformers import pipeline
+from tensorflow.keras.models import load_model
+import tensorflow as tf
+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+from dotenv import load_dotenv
+import openai
+import os
+from langchain.schema import HumanMessage, SystemMessage
+from langchain_openai import ChatOpenAI
+
+
+# In[18]:
+
+
+# Set the model's file path
+file_path = Path("models/model_adam_scaled.h5")
+
+# Load the model to a new object
+adam_5 = tf.keras.models.load_model(file_path)
+
+# Load env variables
+load_dotenv()
+
+# Add your OpenAI API key here
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
+print(f"OpenAI API Key Loaded: {openai_api_key is not None}")
+
+
+# Load the model and tokenizer for translation
+model = MBartForConditionalGeneration.from_pretrained(
+    "facebook/mbart-large-50-many-to-many-mmt"
+)
+tokenizer = MBart50TokenizerFast.from_pretrained(
+    "facebook/mbart-large-50-many-to-many-mmt"
+)
+
+# Set source language
+tokenizer.src_lang = "en_XX"
+
+
+# In[22]:
+
+
+# Constants
+# Language information MBart
+language_info = [
+    "English (en_XX)",
+    "Arabic (ar_AR)",
+    "Czech (cs_CZ)",
+    "German (de_DE)",
+    "Spanish (es_XX)",
+    "Estonian (et_EE)",
+    "Finnish (fi_FI)",
+    "French (fr_XX)",
+    "Gujarati (gu_IN)",
+    "Hindi (hi_IN)",
+    "Italian (it_IT)",
+    "Japanese (ja_XX)",
+    "Kazakh (kk_KZ)",
+    "Korean (ko_KR)",
+    "Lithuanian (lt_LT)",
+    "Latvian (lv_LV)",
+    "Burmese (my_MM)",
+    "Nepali (ne_NP)",
+    "Dutch (nl_XX)",
+    "Romanian (ro_RO)",
+    "Russian (ru_RU)",
+    "Sinhala (si_LK)",
+    "Turkish (tr_TR)",
+    "Vietnamese (vi_VN)",
+    "Chinese (zh_CN)",
+    "Afrikaans (af_ZA)",
+    "Azerbaijani (az_AZ)",
+    "Bengali (bn_IN)",
+    "Persian (fa_IR)",
+    "Hebrew (he_IL)",
+    "Croatian (hr_HR)",
+    "Indonesian (id_ID)",
+    "Georgian (ka_GE)",
+    "Khmer (km_KH)",
+    "Macedonian (mk_MK)",
+    "Malayalam (ml_IN)",
+    "Mongolian (mn_MN)",
+    "Marathi (mr_IN)",
+    "Polish (pl_PL)",
+    "Pashto (ps_AF)",
+    "Portuguese (pt_XX)",
+    "Swedish (sv_SE)",
+    "Swahili (sw_KE)",
+    "Tamil (ta_IN)",
+    "Telugu (te_IN)",
+    "Thai (th_TH)",
+    "Tagalog (tl_XX)",
+    "Ukrainian (uk_UA)",
+    "Urdu (ur_PK)",
+    "Xhosa (xh_ZA)",
+    "Galician (gl_ES)",
+    "Slovene (sl_SI)",
+]
+
+# Convert the information into a dictionary
+language_dict = {}
+for info in language_info:
+    name, code = info.split(" (")
+    code = code[:-1]
+    language_dict[name] = code
+
+# Get the language names for choices in the dropdown
+languages = list(language_dict.keys())
+first_language = languages[0]
+sorted_languages = sorted(languages[1:])
+sorted_languages.insert(0, first_language)
+
+default_language = "English"
+
+# Prediction responses
+malignant_text = "Malignant. Please consult a doctor for further evaluation."
+benign_text = "Benign. Please consult a doctor for further evaluation."
+
+
+# In[23]:
+
+
+# Create instance
+llm = ChatOpenAI(
+    openai_api_key=openai_api_key, model_name="gpt-3.5-turbo", temperature=0
+)
+
+
+# In[24]:
+
+
+# Method to get system and human messages for ChatOpenAI - Predictions
+def get_prediction_messages(prediction_text):
+    # Create a HumanMessage object
+    human_message = HumanMessage(content=f"skin lesion that appears {prediction_text}")
+
+    # Get the system message
+    system_message = SystemMessage(
+        content="You are a medical professional chatting with a patient. You want to provide helpful information and give a preliminary assessment."
+    )
+
+    # Return the system message
+    return [system_message, human_message]
+
+
+# In[25]:
+
+
+# Method to get system and human messages for ChatOpenAI - Help
+def get_chat_messages(chat_prompt):
+    # Create a HumanMessage object
+    human_message = HumanMessage(content=chat_prompt)
+
+    # Get the system message
+    system_message = SystemMessage(
+        content="You are a medical professional chatting with a patient. You want to provide helpful information."
+    )
+    # Return the system message
+    return [system_message, human_message]
+
+
+# In[26]:
+
+
+# Method to predict the image
+def predict_image(language, img):
+    try:
+        try:
+            # Process the image
+            img = img.resize((224, 224))
+            img_array = np.array(img) / 255.0
+            img_array = np.expand_dims(img_array, axis=0)
+        except Exception as e:
+            print(f"Error: {e}")
+            return "There was an error processing the image. Please try again."
+
+        # Get prediction from model
+        prediction = adam_5.predict(img_array)
+        text_prediction = "Malignant" if prediction[0][0] > 0.5 else "Benign"
+
+        try:
+            # Get the system and human messages
+            messages = get_prediction_messages(text_prediction)
+
+            # Get the response from ChatOpenAI
+            result = llm(messages)
+
+            # Get the text prediction
+            text_prediction = (
+                f"Prediction: {text_prediction} Explanation: {result.content}"
+            )
+
+        except Exception as e:
+            print(f"Error: {e}")
+            print(f"Prediction: {text_prediction}")
+            text_prediction = (
+                malignant_text if text_prediction == "Malignant" else benign_text
+            )
+
+        # Get selected language code
+        selected_code = language_dict[language]
+
+        # Check if the target and source languages are the same
+        if selected_code == "en_XX":
+            return (
+                text_prediction,
+                gr.update(visible=False),
+                gr.update(visible=True),
+                gr.update(visible=True),
+                gr.update(visible=True),
+            )
+
+        try:
+            # Encode, generate tokens, decode the prediction
+            encoded_text = tokenizer(text_prediction, return_tensors="pt")
+            generated_tokens = model.generate(
+                **encoded_text,
+                forced_bos_token_id=tokenizer.lang_code_to_id[selected_code],
+            )
+            result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+
+            # Return the result
+            return (
+                result[0],
+                gr.update(visible=False),
+                gr.update(visible=True),
+                gr.update(visible=True),
+                gr.update(visible=True),
+            )
+        except Exception as e:
+            print(f"Error: {e}")
+            return (
+                f"""There was an error processing the translation. 
+            In English:
+            {text_prediction}
+            """,
+                gr.update(visible=False),
+                gr.update(visible=True),
+                gr.update(visible=True),
+                gr.update(visible=True),
+            )
+
+    except Exception as e:
+        print(f"Error: {e}")
+        return (
+            "There was an error processing the request. Please try again.",
+            gr.update(visible=True),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=False),
+        )
+
+
+# In[27]:
+
+
+# Method for on submit
+def on_submit(language, img):
+    print(f"Language: {language}")
+    if language is None or len(language) == 0:
+        language = default_language
+    if img is None:
+        return (
+            "No image uploaded. Please try again.",
+            gr.update(visible=True),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=False),
+        )
+    return predict_image(language, img)
+
+
+# In[28]:
+
+
+# Method for on clear
+def on_clear():
+    return (
+        gr.update(),
+        gr.update(),
+        gr.update(),
+        gr.update(visible=True),
+        gr.update(value=None, visible=False),
+        gr.update(value=None, visible=False),
+        gr.update(visible=False),
+    )
+
+
+# In[29]:
+
+
+# Method for on chat
+def on_chat(language, chat_prompt):
+    try:
+        # Get the system and human messages
+        messages = get_chat_messages(chat_prompt)
+        # Get the response from ChatOpenAI
+        result = llm(messages)
+        # Get the text prediction
+        chat_response = result.content
+
+    except Exception as e:
+        print(f"Error: {e}")
+        return gr.update(
+            value="There was an error processing your question. Please try again.",
+            visible=True,
+        ), gr.update(visible=False)
+
+    # Get selected language code
+    if language is None or len(language) == 0:
+        language = default_language
+    selected_code = language_dict[language]
+    # Check if the target and source languages are the same
+    if selected_code == "en_XX":
+        return gr.update(value=chat_response, visible=True), gr.update(visible=False)
+
+    try:
+        # Encode, generate tokens, decode the prediction
+        encoded_text = tokenizer(chat_response, return_tensors="pt")
+        generated_tokens = model.generate(
+            **encoded_text, forced_bos_token_id=tokenizer.lang_code_to_id[selected_code]
+        )
+        result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+
+        # Return the result
+        return gr.update(value=result[0], visible=True), gr.update(visible=False)
+    except Exception as e:
+        print(f"Error: {e}")
+        return (
+            gr.update(
+                value=f"""There was an error processing the translation. 
+            In English:
+            {chat_response}
+            """,
+                visible=True,
+            ),
+            gr.update(visible=False),
+        )
+
+
+# In[30]:
+
+
+# Gradio app
+
+with gr.Blocks(theme=gr.themes.Default(primary_hue="green")) as demo:
+    intro = gr.Markdown(
+        """
+    # Welcome to Skin Lesion Image Classifier!
+    Select prediction language and upload image to start.
+    """
+    )
+    language = gr.Dropdown(
+        label="Response Language - Default English", choices=sorted_languages
+    )
+    img = gr.Image(image_mode="RGB", type="pil")
+    output = gr.Textbox(label="Results", show_copy_button=True)
+    chat_prompt = gr.Textbox(
+        label="Do you have a question about the results or skin cancer?",
+        placeholder="Enter your question here...",
+        visible=False,
+    )
+    chat_response = gr.Textbox(
+        label="Chat Response", visible=False, show_copy_button=True
+    )
+    submit_btn = gr.Button("Submit", variant="primary", visible=True)
+    chat_btn = gr.Button("Submit Question", variant="primary", visible=False)
+    submit_btn.click(
+        fn=on_submit,
+        inputs=[language, img],
+        outputs=[output, submit_btn, chat_prompt, chat_btn, chat_response],
+    )
+    chat_btn.click(
+        fn=on_chat, inputs=[language, chat_prompt], outputs=[chat_response, chat_btn]
+    )
+    clear_btn = gr.ClearButton(
+        components=[language, img, output, chat_response], variant="stop"
+    )
+    clear_btn.click(
+        fn=on_clear,
+        outputs=[
+            language,
+            img,
+            output,
+            submit_btn,
+            chat_prompt,
+            chat_response,
+            chat_btn,
+        ],
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

@@ -0,0 +1,9 @@
+gradio==4.4.1
+langchain==0.1.17
+langchain-community==0.0.36
+langchain-core==0.1.50
+langchain-openai==0.1.6
+langchain-text-splitters==0.0.1
+python-dotenv==1.0.0
+tensorflow==2.12.0
+transformers==4.40.1