Update app.py

app.py

@@ -1,29 +1,39 @@
-import gradio as gr
-import torch
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-import subprocess
+import gradio
+import requests
+from bs4 import BeautifulSoup
+from transformers import pipeline
 
-#upgrade_command = "python -m pip install --upgrade pip"
+# Function to scrape information from a website
+def scrape_website(prompt, website_link):
+    response = requests.get(website_link)
+    soup = BeautifulSoup(response.content, "html.parser")
+    
+    # Implement your web scraping logic here
+    # Extract the desired information from the HTML
+    
+    scraped_info = "Scraped information from the website"
+    return scraped_info
 
-# Run the command
-#subprocess.run(upgrade_command, shell=True)
+# Function to generate chatbot responses
+def generate_chatbot_response(prompt):
+    chatbot = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
+    chatbot_response = chatbot(prompt, max_length=50, num_return_sequences=1)[0]["generated_text"]
+    return chatbot_response
 
-# Load the pre-trained GPT-2 model and tokenizer
-model_name = "AIDC-ai-business/Marcoroni-70B"  # You can change this to another model if needed
-tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-model = GPT2LMHeadModel.from_pretrained(model_name)
+# Function to handle the web app logic
+def web_app(prompt, website_link):
+    scraped_info = scrape_website(prompt, website_link)
+    chatbot_response = generate_chatbot_response(prompt)
+    return {"Scraped Information": scraped_info, "Chatbot Response": chatbot_response}
 
-def generate_text(prompt):
-    # Encode the input text and generate a continuation
-    input_ids = tokenizer.encode(prompt, return_tensors="pt")
-    output = model.generate(input_ids, max_length=100, num_return_sequences=1, pad_token_id=50256)
-    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
-    
-    # Save the generated text to a text file
-    with open("generated_text.txt", "w") as file:
-        file.write(generated_text)
-    
-    return generated_text
+# Create the Gradio interface
+iface = gradio.Interface(
+    fn=web_app,
+    inputs=["text", "text"],
+    outputs=["text", "text"],
+    title="Web Scraping and Chatbot App",
+    description="Enter a prompt and a website link to scrape information and generate a chatbot response."
+)
 
-iface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
+# Run the Gradio app
 iface.launch()