Create app.py

app.py

@@ -0,0 +1,242 @@
+import streamlit as st
+from huggingface_hub import InferenceClient, AsyncInferenceClient
+from PIL import Image
+from pathlib import Path
+import os, subprocess
+
+st.set_page_config(page_title='HG Inference Client Demo',layout="wide")
+# Cache the header of the app to prevent re-rendering on each load
+@st.cache_resource
+def display_app_header():
+    """Display the header of the Streamlit app."""
+    st.title("1️⃣ HG Inference Client Demo 📊 ")
+    st.subheader("Just a little demontstrator")
+# Display the header of the app
+display_app_header()
+
+# UI sidebar parameters ####################################
+st.sidebar.header("Loging")
+if hg_token :=st.sidebar.text_input('Enter your HG token'):
+    try:
+        subprocess.check_call(["huggingface-cli", "login", "--token", hg_token])
+        st.sidebar.info('Logged', icon="ℹ️") 
+    except subprocess.CalledProcessError:
+        st.sidebar.error('Error with token, try again', icon="⚠️")
+else:
+    st.sidebar.warning("enter your token")
+
+st.sidebar.header("Model")
+selected_model = st.sidebar.radio(
+        "Choose a model or let the client do it",
+        ["Not choose", "Choose"]
+        )
+if selected_model == "Choose":
+    model = st.sidebar.text_input('Enter a model name. ex : facebook/fastspeech2-en-ljspeech')
+else:
+    model = None
+
+st.sidebar.header("Task")
+dict_hg_tasks = {
+"Automatic Speech Recognition":"automatic_speech_recognition",
+"Text-to-Speech (choose model)":"text_to_speech",
+"Image Classification":"image_classification",
+"Image Segmentation":"image_segmentation",
+"Image-to-Text":"image_to_text",
+"Object Detection":"object_detection",
+"Text-to-Image":"text_to_image",
+"Visual Question Answering":"visual_question_answering",
+"Conversational":"conversational",
+"Feature Extraction":"feature_extraction",
+"Question Answering":"question_answering",
+"Summarization":"summarization",
+"Text Classification":"text_classification",
+"Text Generation":"text_generation",
+"Token Classification":"token_classification",
+"Translation (choose model)":"translation",
+}
+
+dict_hg_tasks_params = {
+    "automatic_speech_recognition": {
+        "input": "upload,url",
+        "output": "text",
+        "prompt": False,
+        "context": False
+    },
+    "text_to_speech": {
+        "input": "text",
+        "output": "audio",
+        "prompt": False,
+        "context": False
+    },
+    "image_classification": {
+        "input": "upload,url",
+        "output": "image,text",
+        "prompt": False,
+        "context": False
+    },
+    "image_segmentation": {
+        "input": "upload,url",
+        "output": "image,text",
+        "prompt": False,
+        "context": False
+    },
+    "image_to_text": {
+        "input": "upload,url",
+        "output": "image,text",
+        "prompt": False,
+        "context": False
+    },
+    "object_detection": {
+        "input": "upload,url",
+        "output": "image,text",
+        "prompt": False,
+        "context": False
+    },
+    "text_to_image": {
+        "input": "text",
+        "output": "image",
+        "prompt": False,
+        "context": False
+    },
+    "visual_question_answering": {
+        "input": "upload,url",
+        "output": "image,text",
+        "prompt": True,
+        "context": False
+    },
+    "image_to_image": {
+        "input": "upload,url",
+        "output": "image,text",
+        "prompt": True,
+        "context": False
+    },
+    "feature_extraction": {
+        "input": "text",
+        "output": "text",
+        "prompt": False,
+        "context": False
+    },
+    "conversational": {
+        "input": "text",
+        "output": "text",
+        "prompt": False,
+        "context": False
+    },
+    "question_answering": {
+        "input": None,
+        "output": "text",
+        "prompt": True,
+        "context": True
+    },
+    "text_classification": {
+        "input": "text",
+        "output": "text",
+        "prompt": False,
+        "context": False
+    },
+    "token_classification": {
+        "input": "text",
+        "output": "text",
+        "prompt": False,
+        "context": False
+    },
+    "text_generation": {
+        "input": "text",
+        "output": "text",
+        "prompt": False,
+        "context": False
+    },
+    "text_classification": {
+        "input": "text",
+        "output": "text",
+        "prompt": False,
+        "context": False
+    },
+    "translation": {
+        "input": "text",
+        "output": "text",
+        "prompt": False,
+        "context": False
+    },
+    "summarization": {
+        "input": "text",
+        "output": "text",
+        "prompt": False,
+        "context": False
+    },
+}
+selected_task = st.sidebar.radio(
+        "Choose the task you want to do", # see https://huggingface.co/docs/huggingface_hub/guides/inference"
+        dict_hg_tasks.keys()
+        )
+st.write(f"The current selected task is : {dict_hg_tasks[selected_task]}")
+with st.sidebar.expander("tasks documentation"):
+    st.write("https://huggingface.co/docs/huggingface_hub/package_reference/inference_client")
+
+# functions ########################################
+cwd = os.getcwd()
+def get_input(upload,url,text):
+    if upload is not None:
+        return upload
+    else:
+        if url:
+            return url
+        elif text:
+            return text
+    return None  # Default return if neither upload nor url is provided
+
+def display_inputs(task):
+    if dict_hg_tasks_params[task]["input"] == "upload,url":
+        return st.file_uploader("Choose a file"),st.text_input("or enter a file url"),""
+    elif dict_hg_tasks_params[task]["input"] == "text":
+        return None,"",st.text_input("Enter a text")
+    else:
+        return None,"",""
+    
+def display_prompt(task):
+    if dict_hg_tasks_params[task]["prompt"] is True:
+        return st.text_input("Enter a question")
+    return None
+
+def display_context(task):
+    if dict_hg_tasks_params[task]["context"] is True:
+        return st.text_area("Enter a context")
+    return None
+
+# UI main client ####################################
+
+if selected_task :
+    response = None
+    task = dict_hg_tasks[selected_task]
+    if model:
+        client = InferenceClient(model=model)
+    else:
+        client = InferenceClient()    
+    uploaded_input,url_input,text_input = display_inputs(task)
+    prompt_input = display_prompt(task)
+    context_input = display_context(task)
+    if get_input(uploaded_input,url_input,text_input):
+        input = get_input(uploaded_input,url_input,text_input) 
+        response = getattr(client, task)(input)
+    elif prompt_input:
+        if context_input is not None:
+            response = getattr(client, task)(question=prompt_input,context=context_input)
+        else:
+            response = getattr(client, task)(input,prompt=prompt_input)
+    if response is not None:
+        col1,col2 = st.columns(2)
+        with col1:
+            if "text" in dict_hg_tasks_params[task]["output"]:  
+                st.write(response)
+            elif "audio" in dict_hg_tasks_params[task]["output"]: 
+                Path(os.path.join(cwd,"audio.flac")).write_bytes(response)
+                st.audio(os.path.join(cwd,"audio.flac"))
+        with col2:
+            if dict_hg_tasks_params[task]["output"] == "image,text":
+                image = Image.open(input)
+                st.image(image)
+            elif dict_hg_tasks_params[task]["output"] == "image":
+                response.save(os.path.join(cwd,"generated_image.png"))
+                image = Image.open(os.path.join(cwd,"generated_image.png"))
+                st.image(image)
+