Update app.py

app.py

@@ -1,64 +1,63 @@
 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 deliverable2 import URLValidator
+
+# Instantiate the validator
+validator = URLValidator()
+
+# ✅ Updated queries and URLs
+queries = [
+    "How blockchain works",
+    "Climate change effects",
+    "COVID-19 vaccine effectiveness",
+    "Latest AI advancements",
+    "Stock market trends",
+    "Healthy diet tips",
+    "Space exploration missions",
+    "Electric vehicle benefits",
+    "History of the internet",
+    "Nutritional benefits of a vegan diet",
+    "Mental health awareness",
+]
+
+urls = [
+    "https://www.ibm.com/topics/what-is-blockchain",
+    "https://www.nationalgeographic.com/environment/article/climate-change-overview",
+    "https://www.cdc.gov/coronavirus/2019-ncov/vaccines/effectiveness.html",
+    "https://www.technologyreview.com/topic/artificial-intelligence",
+    "https://www.bloomberg.com/markets",
+    "https://www.healthline.com/nutrition/healthy-eating-tips",
+    "https://www.nasa.gov/missions",
+    "https://www.tesla.com/benefits",
+    "https://www.history.com/topics/inventions/history-of-the-internet",
+    "https://www.hsph.harvard.edu/nutritionsource/healthy-weight/diet-reviews/vegan-diet/",
+    "https://www.who.int/news-room/fact-sheets/detail/mental-health-strengthening-our-response"
+]
+
+# Function to validate URL
+def validate_url(user_query, url_to_check):
+    result = validator.rate_url_validity(user_query, url_to_check)
+
+    if "Validation Error" in result:
+        return {"Error": result["Validation Error"]}
+
+    return {
+        "Content Relevance Score": f"{result['raw_score']['Content Relevance']} / 100",
+        "Bias Score": f"{result['raw_score']['Bias Score']} / 100",
+        "Final Validity Score": f"{result['raw_score']['Final Validity Score']} / 100"
+    }
+
+# Gradio UI
+with gr.Blocks() as app:
+    gr.Markdown("# 🌍 URL Credibility Validator")
+    gr.Markdown("### Validate the credibility of any webpage using AI")
+
+    user_query = gr.Dropdown(queries, label="Select a search query:")
+    url_to_check = gr.Dropdown(urls, label="Select a URL to validate:")
+
+    result_output = gr.JSON(label="Validation Results")
+    
+    submit_button = gr.Button("Validate URL")
+    submit_button.click(validate_url, inputs=[user_query, url_to_check], outputs=result_output)
+
+# Launch the app
+app.launch(server_name="0.0.0.0", server_port=7860)