Update app.py

app.py

@@ -1,64 +1,59 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new 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 (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from transformers import pipeline
+
+# Load the summarization model
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Use the grammar correction model via pipeline
+grammar_correction_pipe = pipeline("text2text-generation", model="pszemraj/flan-t5-large-grammar-synthesis")
+
+# Function for grammar correction
+def correct_grammar(user_input):
+    if user_input.strip():
+        corrected_text = grammar_correction_pipe(user_input)[0]['generated_text']
+        return corrected_text
+    else:
+        return "Please enter some text for grammar correction."
+
+# Function for text summarization
+def summarize_text(user_input):
+    if user_input.strip():
+        summary = summarizer(user_input, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
+        return summary
+    else:
+        return "Please enter some text to summarize."
+
+# Function to combine grammar correction and summarization
+def correct_and_summarize(user_input):
+    corrected_text = correct_grammar(user_input)  # First correct the grammar
+    summary = summarize_text(corrected_text)  # Then summarize the corrected text
+    return summary
+
+# Gradio UI setup
+with gr.Blocks() as demo:
+    gr.Markdown("## Text Summarization and Grammar Correction Assistant")
+
+    # Dropdown to select task
+    task = gr.Dropdown(choices=["Summarize Text", "Correct Grammar"], label="Choose a task")
+
+    # Input component for text
+    user_input = gr.Textbox(label="Enter your text here:")
+
+    # Output box for displaying the result
+    output = gr.Textbox(label="Output", interactive=False)
+
+    # Submit button
+    submit_btn = gr.Button("Submit")
+
+    # Function to process the input based on selected task
+    def process_input(task, user_input):
+        if task == "Summarize Text":
+            return correct_and_summarize(user_input)  # Correct grammar, then summarize
+        elif task == "Correct Grammar":
+            return correct_grammar(user_input)  # Only correct grammar
+
+    # Link the submit button to process the input
+    submit_btn.click(process_input, inputs=[task, user_input], outputs=output)
+
+# Launch the Gradio interface
+demo.launch()