news detector

app.py

@@ -7,17 +7,10 @@ API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-b
 headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
 
 def query(payload):
-    try:
-        response = requests.post(API_URL, headers=headers, json=payload)
-        response.raise_for_status()
-        return response.json()
-    except Exception as e:
-        return [{"generated_text": f"Error: {str(e)}"}]
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
 
 def classify_news(chat_history, user_input):
-    if not user_input or not user_input.strip():
-        return chat_history, ""
-
     prompt = (
         f"You are a fake news classifier. Respond with 'Answer: Real' or 'Answer: Fake'.\n"
         f"Statement: {user_input}"
@@ -30,14 +23,8 @@ def classify_news(chat_history, user_input):
             "do_sample": False
         }
     })
-
     try:
-        model_output = output[0]["generated_text"]
-        # Robust parsing -- finds "Answer: Real" or "Answer: Fake"
-        if "Answer:" in model_output:
-            result = model_output.split("Answer:")[-1].strip().split()[0]
-        else:
-            result = model_output.strip().split()[0]
+        result = output[0]["generated_text"].split("Answer:")[-1].strip().split()[0]
         if result.lower().startswith("real"):
             result = "🟢 Real"
         elif result.lower().startswith("fake"):
@@ -46,39 +33,45 @@ def classify_news(chat_history, user_input):
             result = "⚠️ Unclear"
     except Exception:
         result = "❌ Error: Could not get a response."
-
-    chat_history = chat_history or []
-    chat_history.append({"role": "user", "content": user_input})
-    chat_history.append({"role": "assistant", "content": result})
+    chat_history.append((user_input, result))
     return chat_history, ""
 
-def export_chat(chat_history):
-    export_text = ""
-    for message in chat_history:
-        role = "User" if message["role"] == "user" else "Model"
-        export_text += f"{role}: {message['content']}\n"
-    with open("chat_log.txt", "w") as f:
-        f.write(export_text.strip())
-    return "chat_log.txt"
-
-with gr.Blocks(theme=gr.themes.Soft(), css="""...""") as demo:
+with gr.Blocks(theme=gr.themes.Soft(), css="""
+.chatbot {background-color: #0d1117; border-radius: 10px; padding: 10px;}
+.message.user {background-color: #1f6feb; color: white; padding: 8px; border-radius: 10px;}
+.message.bot {background-color: #21262d; color: #c9d1d9; padding: 8px; border-radius: 10px;}
+#submit-button {background-color: #2ea043; color: white; font-weight: bold; transition: background-color 0.3s ease-in-out;}
+#submit-button:hover {background-color: #239139;}
+#clear-button {background-color: #e5534b; color: white; font-weight: bold; transition: background-color 0.3s ease-in-out;}
+#clear-button:hover {background-color: #c8433b;}
+.toggle-label {color: #c9d1d9; margin-top: 1rem;}
+""") as demo:
     gr.Markdown("""
     # 📰 Fake News Detector Chat
     _Classify news as **Real** or **Fake** using a GPT-style model._
     """)
 
-    chatbot = gr.Chatbot(type="messages", label="🧠 Chatbot", elem_classes=["chatbot"])
+    chatbot = gr.Chatbot(type="messages",label="🧠 Chatbot", elem_classes=["chatbot"])
     with gr.Row():
         user_input = gr.Textbox(placeholder="Type or paste a news statement here...", scale=6)
         clear_btn = gr.Button("🧹 Clear", elem_id="clear-button")
+
     submit_btn = gr.Button("🚀 Classify", elem_id="submit-button", scale=2)
+
     with gr.Row():
         audio = gr.Audio(type="filepath", label="🎤 Speak News", interactive=True)
         export_btn = gr.Button("⬇️ Export Chat")
+
     toggle_dark_mode = gr.Checkbox(label="🌗 Toggle Dark Mode", value=True)
 
+    def export_chat(chat_history):
+        export_text = "\n".join([f"User: {u}\nModel: {r}" for u, r in chat_history])
+        with open("chat_log.txt", "w") as f:
+            f.write(export_text)
+        return "chat_log.txt"
+
     submit_btn.click(classify_news, inputs=[chatbot, user_input], outputs=[chatbot, user_input])
-    clear_btn.click(lambda: ([], ""), None, [chatbot, user_input])
+    clear_btn.click(lambda: [], None, chatbot)
     export_btn.click(export_chat, inputs=[chatbot], outputs=gr.File(label="Download Log"))
 
     demo.launch()

requirements.txt

@@ -1,2 +1,2 @@
-gradio>=4.17.0
+gradio>=3.50.2
 requests