Update app.py

app.py

@@ -4,6 +4,7 @@ import torch
 import requests
 from bs4 import BeautifulSoup
 import matplotlib.pyplot as plt
+import openai
 import os
 
 # Load model and tokenizer
@@ -11,11 +12,13 @@ model_name = "mrm8488/bert-tiny-finetuned-fake-news-detection"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-# Initialize verdict counters
+# Verdict counters
 verdict_counts = {"Authentic": 0, "Possibly Misinformation": 0}
 
-# Read API key from environment variable
+# API keys
 FACT_CHECK_API_KEY = os.getenv("FACT_CHECK_API_KEY")
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+openai.api_key = OPENAI_API_KEY
 
 def extract_text_from_url(url):
     try:
@@ -64,11 +67,28 @@ def fact_check_google_api(query, api_key):
     except Exception as e:
         return f"Error calling Fact Check API: {e}"
 
+def gpt_fact_check(prompt):
+    if not OPENAI_API_KEY:
+        return "OpenAI API key not found. Please set OPENAI_API_KEY in environment."
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",  # or "gpt-4" if you have access
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant for fact-checking news articles. Analyze the following content for misinformation, summarize the main claim, and explain your reasoning."},
+                {"role": "user", "content": prompt}
+            ],
+            max_tokens=300,
+            temperature=0.2,
+        )
+        return response['choices'][0]['message']['content'].strip()
+    except Exception as e:
+        return f"OpenAI API error: {e}"
+
 def detect_misinformation(input_text, input_type):
     if input_type == "URL":
         input_text = extract_text_from_url(input_text)
         if input_text.startswith("Error"):
-            return input_text, "Error", 0.0, update_chart(), "URL extraction failed."
+            return input_text, "Error", 0.0, update_chart(), "URL extraction failed.", ""
 
     inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
@@ -78,7 +98,8 @@ def detect_misinformation(input_text, input_type):
         verdict = "Possibly Misinformation" if score > 0.5 else "Authentic"
         verdict_counts[verdict] += 1
         fact_check_result = fact_check_google_api(input_text, FACT_CHECK_API_KEY)
-        return input_text[:1000], verdict, round(score * 100, 2), update_chart(), fact_check_result
+        gpt_result = gpt_fact_check(input_text)
+        return input_text[:1000], verdict, round(score * 100, 2), update_chart(), fact_check_result, gpt_result
 
 with gr.Blocks() as demo:
     gr.Markdown("## 🧠 Misinformation Detection Dashboard")
@@ -93,6 +114,7 @@ with gr.Blocks() as demo:
     score = gr.Label(label="Authenticity Score (%)")
     chart = gr.Plot(label="Analytics Dashboard")
     fact_check = gr.Textbox(label="Fact Check Results", lines=6)
+    gpt_fact = gr.Textbox(label="OpenAI GPT Analysis", lines=6)
 
     btn = gr.Button("Analyze")
 
@@ -104,6 +126,10 @@ with gr.Blocks() as demo:
                 mode = "Text"
         return detect_misinformation(text, mode)
 
-    btn.click(fn=handle_input, inputs=[input_text, input_type], outputs=[output_text, verdict, score, chart, fact_check])
+    btn.click(
+        fn=handle_input,
+        inputs=[input_text, input_type],
+        outputs=[output_text, verdict, score, chart, fact_check, gpt_fact]
+    )
 
 demo.launch()