initial commit

app.py

@@ -1,7 +1,103 @@
+import numpy as np
 import gradio as gr
+import os
+import openai
+import newspaper
+import json
+import re
+from transformers import GPT2Tokenizer
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def flip_text(name):
+    return "Hello " + name + "!"
+demo = gr.Interface(fn=flip_text, inputs="text", outputs="text")
+
+
+
+# define the text summarizer function
+def flip_text(request, page_url, contraseña, temp):
+    try:
+        page = newspaper.Article(url=page_url)
+        page.download()
+        page.parse()
+    except Exception as e:
+        return "", f"--- An error occurred while processing the URL: {e} ---", ""
+
+    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+    tokens = tokenizer.tokenize(page.text)
+    num_tokens = len(tokens)
+
+    if num_tokens > 10 and num_tokens < 2000:
+        openai.api_key = contraseña
+        # get the response from openai API
+        try:
+            response = openai.Completion.create(
+                engine="text-davinci-003",
+                prompt=request + "\n\n" + page.text,
+                max_tokens=2048,
+                temperature=temp,
+                top_p=0.9,
+            )
+            # get the response text
+            response_text = response.choices[0].text
+            # clean the response text
+            response_text = re.sub(r'\s+', ' ', response_text)
+            return page.text, response_text, num_tokens
+        except Exception as e:
+            return page.text, f"--- An error occurred while processing the request: {e} ---", num_tokens
+    return page.text, "--- Max number of tokens ---", num_tokens
+
+# define the gradio interface
+iface = gr.Interface(
+    fn=flip_text,
+    inputs=[gr.Textbox(lines=1, placeholder="Enter your prompt here...", label="Prompt:", type="text"),
+            gr.Textbox(lines=1, placeholder="Enter the URL here...", label="URL to parse:", type="text"),
+            gr.Textbox(lines=1, placeholder="Enter your API-key here...", label="API-Key:", type="password"),
+            gr.Slider(0.0,1.0, value=0.3, label="Temperature:")
+            ],
+    outputs=[gr.Textbox(label="Input:"), gr.Textbox(label="Output:"), gr.Textbox(label="Tokens:")],
+    examples=[["Summarize the following text as a list:","https://blog.google/outreach-initiatives/google-org/our-commitment-on-using-ai-to-accelerate-progress-on-global-development-goals/","",0.3],
+            ["Generate a summary of the following text. Give me an overview of main business impact from the text following this template:\n- Summary:\n- Business Impact:\n- Companies:", "https://ai.googleblog.com/2019/10/quantum-supremacy-using-programmable.html","",0.7]
+    ],
+    title="Information Extraction with ChatGPT Interface:",
+    # description="This tool allows querying the text retrieved from the URL using OpenAI's [text-davinci-003] engine.\nThe URL text can be referenced in the prompt as \"following text\".\nA GPT2 tokenizer is included to ensure that the 2000 token limit for OpenAI queries is not exceeded. Provide a prompt with your request, the url for text retrieval, your api-key and temperature to process the text."
+)
+
+
+
+error_message = ""
+
+try:
+    iface.launch()
+except Exception as e:
+    error_message = "An error occurred: " + str(e)
+    iface.outputs[1].value = error_message
+
+
+
+
+def flip_image(x):
+    return np.fliplr(x)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("Flip text or image files using this demo.")
+    with gr.Tab("Flip Text"):
+        text_input = gr.Textbox()
+        text_output = gr.Textbox()
+        text_button = gr.Button("Flip")
+    with gr.Tab("Flip Image"):
+        with gr.Row():
+            image_input = gr.Image()
+            image_output = gr.Image()
+        image_button = gr.Button("Flip")
+
+    with gr.Accordion("Open for More!"):
+        gr.Markdown("Look at me...")
+
+    text_button.click(flip_text, inputs=text_input, outputs=text_output)
+    image_button.click(flip_image, inputs=image_input, outputs=image_output)
+    
+if __name__ == "__main__":
+    demo.launch()