Update app.py

app.py

@@ -1,73 +1,28 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-import pytesseract
-from pypdf import PdfReader
-import ocrmypdf
-from PIL import Image
-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 = "google/gemma-3-27b-it"
-#model_text = "google/gemma-2-27b-it"
-#model_vision = "google/paligemma2-3b-pt-224"
+#model_text = "google/gemma-3-27b-it"
+model_text = "google/gemma-2-27b-it"
 
 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": system_prompt}]
     #history.append(messages[0])
     #messages.append({"role": "user", "content": prompt})
     #history.append(messages[1])
 
-    messages = [{"role": "user", "content": prompt + " " + output}]
+    messages = [{"role": "user", "content": prompt}]
     history.append(messages[0])
     
     stream = client.chat.completions.create(
@@ -88,7 +43,6 @@ 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"),
@@ -97,57 +51,8 @@ app_text = gr.ChatInterface(
     title = "Google Gemma",
     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 = "Google Gemma",
-    description = model_vision,
-)
-"""
 app = gr.TabbedInterface(
     [app_text],
     ["Text"]
-).launch()
-
-#if __name__ == "__main__":
-#    app.launch()
+).launch()