Create development/app

development/app

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+import gradio as gr
+from huggingface_hub import InferenceClient
+import pytesseract
+from PIL import Image
+from pypdf import PdfReader
+import ocrmypdf
+import os
+
+# Image to Text
+
+def fn_image_to_text(input_image):
+    return pytesseract.image_to_string(Image.open(input_image))
+
+# PDF to Text
+
+def fn_pdf_to_text(input_pdf):
+    reader = PdfReader(input_pdf)
+    
+    output_pdf = ""
+    for page in reader.pages:
+        output_pdf+=page.extract_text()
+  
+    image_count = 0
+    for page in reader.pages:
+        image_count += len(page.images)
+
+    if image_count > 0 and len(output_pdf) < 1000:
+        input_pdf_ocr = input_pdf.replace(".pdf", " - OCR.pdf")
+        ocrmypdf.ocr(input_pdf, input_pdf_ocr, force_ocr=True)
+    
+        reader = PdfReader(input_pdf_ocr)
+        output_pdf = ""
+        for page in reader.pages:
+            output_pdf+=page.extract_text()
+
+        os.remove(input_pdf_ocr)
+  
+    return output_pdf
+
+# Inference
+
+model_text = "meta-llama/Llama-3.2-3B-Instruct"
+model_vision = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+
+client = InferenceClient()
+
+def fn_text(
+    prompt,
+    history,
+    input,
+    #system_prompt,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    if input:
+        if os.path.splitext(input)[1].lower() in [".png", ".jpg", ".jpeg"]:
+            output = fn_image_to_text(input)
+        if os.path.splitext(input)[1].lower() == ".pdf":
+            output = fn_pdf_to_text(input)
+    else:
+        output = ""
+    
+    messages = [{"role": "system", "content": [{"type": "text", "text": output}]}]
+    #messages = [{"role": "system", "content": [{"type": "text", "text": system_prompt}]}]
+    history.append(messages[0])
+    
+    messages.append({"role": "user", "content": [{"type": "text", "text": prompt}]})
+    history.append(messages[1])
+    
+    stream = client.chat.completions.create(
+        model = model_text,
+        messages = history,
+        max_tokens = max_tokens,
+        temperature = temperature,
+        top_p = top_p,
+        stream = True,
+    )
+    
+    chunks = []
+    for chunk in stream:
+        chunks.append(chunk.choices[0].delta.content or "")
+        yield "".join(chunks)
+
+app_text = gr.ChatInterface(
+    fn = fn_text,
+    type = "messages",
+    additional_inputs = [
+        gr.File(type="filepath", label="Input"),
+        #gr.Textbox(value="You are a helpful assistant.", label="System Prompt"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-P"),
+    ],
+    title = "Meta Llama",
+    description = model_text,
+)
+
+def fn_vision(
+    prompt,
+    image_url,
+    #system_prompt,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "user", "content": [{"type": "text", "text": prompt}]}]
+    
+    if image_url:
+        messages[0]["content"].append({"type": "image_url", "image_url": {"url": image_url}})
+    
+    stream = client.chat.completions.create(
+        model = model_vision,
+        messages = messages,
+        max_tokens = max_tokens,
+        temperature = temperature,
+        top_p = top_p,
+        stream = True,
+    )
+    
+    chunks = []
+    for chunk in stream:
+        chunks.append(chunk.choices[0].delta.content or "")
+        yield "".join(chunks)
+
+app_vision = gr.Interface(
+    fn = fn_vision,
+    inputs = [
+        gr.Textbox(label="Prompt"),
+        gr.Textbox(label="Image URL")
+    ],
+    outputs = [
+        gr.Textbox(label="Output")
+    ],
+    additional_inputs = [
+        #gr.Textbox(value="You are a helpful assistant.", label="System Prompt"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-P"),
+    ],
+    title = "Meta Llama",
+    description = model_vision,
+)
+
+app = gr.TabbedInterface(
+    [app_text, app_vision],
+    ["Text", "Vision"]
+).launch()
+
+#if __name__ == "__main__":
+#    app.launch()