Update app.py

app.py

@@ -10,11 +10,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 # Initialize vLLM with CPU configuration
 vllm_model = LLM(model=model_name, tensor_parallel_size=1, device="cpu")
 
-# Load the OCR model and processor
-ocr_model_name = "microsoft/trocr-small-handwritten"
-ocr_model = VisionEncoderDecoderModel.from_pretrained(ocr_model_name)
-ocr_processor = TrOCRProcessor.from_pretrained(ocr_model_name)
-#ocr_processor = AutoProcessor.from_pretrained(ocr_model_name)
+ 
 
 def generate_response(prompt, max_tokens, temperature, top_p):
     # Define sampling parameters
@@ -31,62 +27,11 @@ def generate_response(prompt, max_tokens, temperature, top_p):
     generated_text = output[0].outputs[0].text
     return generated_text
 
-def ocr_image(image_path):
-    # Open the image from the file path
-    image = Image.open(image_path).convert("RGB")
-    
-    # Preprocess the image for the OCR model
-    pixel_values = ocr_processor(images=image, return_tensors="pt").pixel_values
-    
-    # Perform OCR inference
-    outputs = ocr_model.generate(pixel_values)
-    
-    # Decode the generated tokens into text
-    text = ocr_processor.batch_decode(outputs, skip_special_tokens=True)[0]
-    return text
+ 
 
 # Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("# 🚀 Hugging Face Integration with vLLM and OCR (CPU)")
-    gr.Markdown("Upload an image to extract text using OCR or generate text using the vLLM integration.")
-
-    with gr.Tab("Text Generation"):
-        with gr.Row():
-            with gr.Column():
-                prompt_input = gr.Textbox(
-                    label="Prompt",
-                    placeholder="Enter your prompt here...",
-                    lines=3,
-                )
-                max_tokens = gr.Slider(
-                    label="Max Tokens",
-                    minimum=10,
-                    maximum=500,
-                    value=100,
-                    step=10,
-                )
-                temperature = gr.Slider(
-                    label="Temperature",
-                    minimum=0.1,
-                    maximum=1.0,
-                    value=0.7,
-                    step=0.1,
-                )
-                top_p = gr.Slider(
-                    label="Top P",
-                    minimum=0.1,
-                    maximum=1.0,
-                    value=0.9,
-                    step=0.1,
-                )
-                submit_button = gr.Button("Generate")
-            
-            with gr.Column():
-                output_text = gr.Textbox(
-                    label="Generated Text",
-                    lines=10,
-                    interactive=False,
-                )
+    
         
         submit_button.click(
             generate_response,
@@ -116,6 +61,11 @@ with gr.Blocks() as demo:
             inputs=[image_input],
             outputs=ocr_output,
         )
+prompt =gr.Textbox()
+max_tokens = gr.Textbox()
+temperature = gr.Textbox()
+top_p = gr.Textbox()
+demo=gr.Interface(generate_response, inputs=[prompt, max_tokens,temperature, top_p], outputs="text")
 
 # Launch the app
 demo.launch()