Update app.py

app.py

@@ -1,19 +1,19 @@
 import streamlit as st
 import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-from deep_translator import GoogleTranslator
+from transformers import AutoTokenizer, AutoModelForSequenceClassification,pipeline
+
 import requests
-import plotly.graph_objects as go
+import json
+
 
-# Load models
+st.set_page_config(page_title="News Prediction", page_icon=":earth_africa:")
+
+# Load tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("hamzab/roberta-fake-news-classification")
 model = AutoModelForSequenceClassification.from_pretrained("hamzab/roberta-fake-news-classification")
 
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-
-st.set_page_config(page_title="Fake News Detector", page_icon="📰")
 
-# --- Functions ---
+from deep_translator import GoogleTranslator
 
 def translate_to_english(text):
     try:
@@ -21,11 +21,7 @@ def translate_to_english(text):
     except Exception as e:
         return f"Error in translation: {e}"
 
-def summarize_text(text):
-    if len(text) > 100:
-        summary = summarizer(text[:1024], max_length=80, min_length=30, do_sample=False)
-        return summary[0]['summary_text']
-    return text
+
 
 def predict_fake(title, text):
     input_str = "<title>" + title + "<content>" + text + "<end>"
@@ -34,109 +30,126 @@ def predict_fake(title, text):
     model.to(device)
     with torch.no_grad():
         output = model(input_ids["input_ids"].to(device), attention_mask=input_ids["attention_mask"].to(device))
-    probs = torch.nn.Softmax(dim=1)(output.logits)[0]
-    return {"Fake": probs[0].item(), "Real": probs[1].item()}
-
-def render_confidence_chart(confidence):
-    fig = go.Figure(data=[go.Pie(
-        values=[confidence, 100 - confidence],
-        labels=['Confidence', 'Uncertainty'],
-        hole=0.6,
-        marker_colors=[
-            f'hsl({confidence * 1.2}, 70%, 50%)',
-            'rgb(240, 240, 240)'
-        ],
-        textinfo='label+percent',
-        hoverinfo='label+value'
-    )])
-    fig.update_layout(
-        showlegend=False,
-        margin=dict(t=10, b=10, l=10, r=10),
-        annotations=[dict(text=f'{confidence}%', x=0.5, y=0.5, font_size=20, showarrow=False)],
-        height=300
-    )
-    st.plotly_chart(fig, use_container_width=True)
-
-def simulate_detected_patterns(text):
-    patterns = []
-    if "breaking" in text.lower():
-        patterns.append({"phrase": "breaking", "category": "clickbait", "impact": -5})
-    if "confirmed" in text.lower():
-        patterns.append({"word": "confirmed", "category": "assertive", "impact": 5})
-    if "shocking" in text.lower():
-        patterns.append({"word": "shocking", "category": "exaggeration", "impact": -10})
-    return patterns
+    return dict(zip(["Fake", "Real"], [x.item() for x in list(torch.nn.Softmax()(output.logits)[0])]))
 
 def fact_check_with_google(api_key, query):
     url = f"https://factchecktools.googleapis.com/v1alpha1/claims:search"
-    params = {"query": query, "key": api_key}
+    params = {
+        "query": query,
+        "key": api_key
+    }
     response = requests.get(url, params=params)
-    return response.json() if response.status_code == 200 else {"error": f"Error: {response.status_code}"}
-
-# --- App UI ---
-
-st.title("📰 Fake News Detection App")
-st.markdown("Enter a news article to predict its credibility and view confidence metrics.")
-
-with st.form("news_form"):
-    title = st.text_input("📝 Title")
-    text = st.text_area("📜 Content")
-    language = st.selectbox("🌐 Language of Input", ["English", "Other"])
-    summarize_option = st.checkbox("🧠 Summarize before fact check")
-    check_fact = st.checkbox("🔍 Check with Google Fact Check")
-    GOOGLE_API_KEY = st.text_input("🔑 Google API Key (optional)", type="password")
-    submit_button = st.form_submit_button("🚀 Predict")
-
-if submit_button:
-    if language != "English":
-        title = translate_to_english(title)
-        text = translate_to_english(text)
-    
-    if summarize_option:
-        summary = summarize_text(title + " " + text)
-        st.markdown("### ✂️ Summary Used for Fact Check")
-        st.info(summary)
+    if response.status_code == 200:
+        return response.json()
     else:
-        summary = title + " " + text
-
-    prediction = predict_fake(title, text)
-    confidence = round(prediction["Real"] * 100)
-    verdict = "Real" if confidence > 60 else "Fake" if confidence < 40 else "Uncertain"
-    color = "green" if verdict == "Real" else "red" if verdict == "Fake" else "orange"
-
-    # Output
-    st.subheader("✅ Prediction Result")
-    st.markdown(f"""
-    <div style='background-color:#f7f9fc;padding:1rem;border-radius:10px'>
-        <b>Verdict:</b> <span style='color:{color}; font-weight:600'>{verdict}</span><br>
-        <b>Confidence:</b> {confidence}%
-    </div>
-    """, unsafe_allow_html=True)
-
-    render_confidence_chart(confidence)
-
-    # Detected Patterns
-    patterns = simulate_detected_patterns(title + " " + text)
-    if patterns:
-        st.subheader("🔎 Detected Language Patterns")
-        for p in patterns:
-            word = p.get("word") or p.get("phrase")
-            st.markdown(f"- **{word}** ({p['category']}) — "
-                        f"<span style='color:{'green' if p['impact']>0 else 'red'};'>{p['impact']:+}</span>",
-                        unsafe_allow_html=True)
-
-    GOOGLE_API_KEY = "AIzaSyAf5v5380xkpo0Rk3kBiSxpxYVBQwcDi2A"  
-    # Google Fact Check
-    if check_fact and GOOGLE_API_KEY:
-        st.subheader("🌐 Google Fact Check Results")
-        facts = fact_check_with_google(GOOGLE_API_KEY, summary)
-        if "claims" in facts:
-            for claim in facts["claims"]:
-                st.markdown(f"**Claim:** {claim.get('text', 'N/A')}")
-                for review in claim.get("claimReview", []):
-                    st.write(f"- **Publisher**: {review.get('publisher', {}).get('name', 'N/A')}")
-                    st.write(f"- **Rating**: {review.get('textualRating', 'N/A')}")
-                    st.write(f"- **URL**: {review.get('url', 'N/A')}")
-                    st.write("---")
+        return {"error": f"Unable to fetch results from Google Fact Check API. HTTP {response.status_code}: {response.text}"}
+
+'''def main():
+    st.title("Fake News Prediction")
+
+    # Load Google API key from a secure location or environment variable
+    
+
+    # Create the form for user input
+    with st.form("news_form"):
+        st.subheader("Enter News Details")
+        title = st.text_input("Title")
+        text = st.text_area("Text")
+        language = st.selectbox("Select Language", options=["English", "Other"])
+        submit_button = st.form_submit_button("Submit")
+
+    # Process form submission and make prediction
+    if submit_button:
+        if language == "Other":
+            title = translate_to_english(title)
+            text = translate_to_english(text)
+
+        prediction = predict_fake(title, text)
+
+        st.subheader("Prediction:")
+        st.write("Prediction: ", prediction)
+
+        if prediction.get("Real") > 0.5:
+            st.write("This news is predicted to be **real** :muscle:")
         else:
-            st.warning("No fact-check results found.")
+            st.write("This news is predicted to be **fake** :shit:")
+     
+            '''
+# Load summarizer
+@st.cache_resource
+def load_summarizer():
+    return pipeline("summarization", model="facebook/bart-large-cnn")
+
+summarizer = load_summarizer()
+
+
+
+def summarize_text(text):
+    try:
+        summary = summarizer(text, max_length=30, min_length=5, do_sample=False)
+        return summary[0]['summary_text']
+    except Exception as e:
+        return f"Error in summarization: {e}"
+
+
+def main():
+    st.title("Fake News Prediction")
+
+    # Store your API key here or load from environment variable
+    GOOGLE_API_KEY = "AIzaSyAf5v5380xkpo0Rk3kBiSxpxYVBQwcDi2A"  # 🔐 Replace this!
+
+    with st.form("news_form"):
+        st.subheader("Enter News Details")
+        title = st.text_input("Title")
+        text = st.text_area("Text")
+        language = st.selectbox("Select Language", options=["English", "Other"])
+        check_fact = st.checkbox("Also check with Google Fact Check API")
+        submit_button = st.form_submit_button("Submit")
+
+    if submit_button:
+        if language == "Other":
+            title = translate_to_english(title)
+            text = translate_to_english(text)
+
+        prediction = predict_fake(title, text)
+
+        st.subheader("Prediction:")
+        st.write("Prediction: ", prediction)
+
+        if prediction.get("Real") > 0.5:
+            st.write("This news is predicted to be **real** :muscle:")
+        else:
+            st.write("This news is predicted to be **fake** :shit:")
+
+
+        if check_fact and GOOGLE_API_KEY:
+            st.subheader("Google Fact Check Results")
+        
+            # Optional: user-provided claim input
+           # custom_claim = st.text_input("Optional: Enter a specific claim to fact-check", "")
+           # query = custom_claim if custom_claim else title  # Use custom claim if provided
+            summarized_claim = summarize_text(title)
+            st.info(f"🔍 Fact check query (summarized): **{summarized_claim}**")
+        
+            fact_check_data = fact_check_with_google(GOOGLE_API_KEY, summarized_claim)
+        
+            # Optional: show raw data for debugging
+            # st.json(fact_check_data)
+        
+
+
+            if "claims" in fact_check_data and len(fact_check_data["claims"]) > 0:
+                for claim in fact_check_data["claims"]:
+                    st.markdown(f"**Claim:** {claim.get('text', 'N/A')}")
+                    for review in claim.get("claimReview", []):
+                        st.write(f"- **Publisher**: {review.get('publisher', {}).get('name', 'N/A')}")
+                        st.write(f"- **Rating**: {review.get('textualRating', 'N/A')}")
+                        st.write(f"- **URL**: {review.get('url', 'N/A')}")
+                        st.write("---")
+            else:
+                st.warning("No fact-check results found. Try changing the title or query.")
+
+       
+if __name__ == "__main__":
+    main()
+