Update app.py

app.py

@@ -1,202 +1,16 @@
 
 
-# app.py v2
+# # app.py v2
 
-import os
-import re
-import fitz  # PyMuPDF
-import tempfile
-from datetime import datetime
-import base64
-from gtts import gTTS
-import streamlit as st
-from transformers.pipelines import pipeline
-from groq import Groq
-
-# ✅ Hugging Face and GROQ secrets loaded via Hugging Face Spaces Secrets interface
-
-# ⛳ Access secrets securely from environment variables
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-HF_TOKEN = os.getenv("HF_TOKEN")
-KAGGLE_USERNAME = os.getenv("KAGGLE_USERNAME")
-KAGGLE_KEY = os.getenv("KAGGLE_KEY")
-
-# ✅ Validate secrets
-if not all([GROQ_API_KEY, HF_TOKEN, KAGGLE_USERNAME, KAGGLE_KEY]):
-    st.error("❌ One or more required API keys are missing from the environment.")
-    st.stop()
-
-# ✅ Initialize Groq client
-client = Groq(api_key=GROQ_API_KEY)
-
-# ✅ Load phishing detection pipeline from Hugging Face
-phishing_pipe = pipeline(
-    "text-classification",
-    model="ealvaradob/bert-finetuned-phishing",
-    token=HF_TOKEN
-)
-
-# ✅ Language and role options
-language_choices = ["English", "Urdu", "French"]
-role_choices = ["Admin", "Procurement", "Logistics"]
-
-# ✅ Glossary terms
-GLOSSARY = {
-    "phishing": "Phishing is a scam where attackers trick you into revealing personal information.",
-    "malware": "Malicious software designed to harm or exploit systems.",
-    "spam": "Unwanted or unsolicited messages.",
-    "tone": "The emotional character of the message."
-}
-
-# ✅ Translations (demo dictionary-based)
-TRANSLATIONS = {
-    "Phishing": {"Urdu": "فشنگ", "French": "Hameçonnage"},
-    "Spam": {"Urdu": "سپیم", "French": "Courrier indésirable"},
-    "Malware": {"Urdu": "میلویئر", "French": "Logiciel malveillant"},
-    "Safe": {"Urdu": "محفوظ", "French": "Sûr"}
-}
-
-# =======================
-# Streamlit UI
-# =======================
-st.set_page_config(page_title="ZeroPhish Gate", layout="wide")
-st.title("🛡️ ZeroPhish Gate")
-st.markdown("AI-powered phishing message detection and explanation.")
-
-# Input fields
-col1, col2 = st.columns([3, 1])
-with col1:
-    text_input = st.text_area("✉️ Paste Suspicious Message", height=200)
-    uploaded_file = st.file_uploader("📄 Upload PDF/TXT (optional)", type=["pdf", "txt"])
-
-with col2:
-    language = st.selectbox("🌐 Preferred Language", language_choices)
-    role = st.selectbox("🧑‍💼 Your Role", role_choices)
-
-analyze_btn = st.button("🔍 Analyze with AI")
-
-# =======================
-# Function Definitions
-# =======================
-def extract_text_from_file(file):
-    if file is None:
-        return ""
-    ext = file.name.split(".")[-1].lower()
-    if ext == "pdf":
-        doc = fitz.open(stream=file.read(), filetype="pdf")
-        return "\n".join(page.get_text() for page in doc)
-    elif ext == "txt":
-        return file.read().decode("utf-8")
-    return ""
-
-def analyze_with_huggingface(text):
-    try:
-        result = phishing_pipe(text)
-        label = result[0]['label']
-        confidence = round(result[0]['score'] * 100, 2)
-        threat_type = {
-            "PHISHING": "Phishing",
-            "SPAM": "Spam",
-            "MALWARE": "Malware",
-            "LEGITIMATE": "Safe"
-        }.get(label.upper(), "Unknown")
-        return label, confidence, threat_type
-    except Exception as e:
-        return "Error", 0, f"Error: {e}"
-
-def semantic_analysis(text):
-    response = client.chat.completions.create(
-        model="llama3-8b-8192",
-        messages=[
-            {"role": "system", "content": "You are a cybersecurity assistant."},
-            {"role": "user", "content": f"Please explain this message in professional tone for a {role} in {language}. Do not end with questions.\n\nMessage:\n{text}"}
-        ]
-    )
-    return response.choices[0].message.content
-
-def translate_label(threat_type):
-    return TRANSLATIONS.get(threat_type, {}).get(language, threat_type)
-
-def text_to_speech(text):
-    tts = gTTS(text=text, lang='en')
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
-        tts.save(fp.name)
-        return fp.name
-
-def create_report(label, score, threat_type, explanation, text):
-    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"Zerophish_Report_{ts}.txt"
-    report = f"""
-🔍 AI Threat Detection Report
-
-Input Message:
-{text}
-
-Prediction: {label}
-Threat Type: {threat_type}
-Confidence: {score}%
-
----
-
-🧠 Explanation:
-{explanation}
-"""
-    with open(filename, "w") as f:
-        f.write(report)
-    return filename
-
-# =======================
-# Run Analysis
-# =======================
-if analyze_btn:
-    combined_text = text_input
-    if uploaded_file:
-        extracted = extract_text_from_file(uploaded_file)
-        combined_text += "\n" + extracted
-
-    if not combined_text.strip():
-        st.warning("⚠️ Please enter some text or upload a file to analyze.")
-    else:
-        label, score, threat_type = analyze_with_huggingface(combined_text)
-        translated_threat = translate_label(threat_type)
-
-        st.subheader("🔍 AI Threat Detection Result")
-        st.markdown(f"**Prediction:** {label}")
-        st.markdown(f"**Threat Type:** {threat_type} ({translated_threat})")
-        st.markdown(f"**Confidence:** {score}%")
-
-        explanation = ""
-        if threat_type.lower() != "safe":
-            with st.expander("🧠 Semantic Reanalysis by LLaMA"):
-                explanation = semantic_analysis(combined_text)
-                st.write(explanation)
-
-                if st.button("🔊 Play Explanation as Audio"):
-                    audio_path = text_to_speech(explanation)
-                    with open(audio_path, "rb") as f:
-                        st.audio(f.read(), format="audio/mp3")
-
-        with st.expander("📜 Glossary Help"):
-            for term, definition in GLOSSARY.items():
-                st.markdown(f"**{term.capitalize()}**: {definition}")
-
-        if explanation:
-            report_path = create_report(label, score, threat_type, explanation, combined_text)
-            with open(report_path, "rb") as f:
-                b64 = base64.b64encode(f.read()).decode()
-                href = f'<a href="data:file/txt;base64,{b64}" download="{report_path}">📄 Download Full Report</a>'
-                st.markdown(href, unsafe_allow_html=True)
-
-#app v3
 # import os
 # import re
 # import fitz  # PyMuPDF
 # import tempfile
-# import base64
 # from datetime import datetime
+# import base64
 # from gtts import gTTS
 # import streamlit as st
-# from transformers import pipeline
+# from transformers.pipelines import pipeline
 # from groq import Groq
 
 # # ✅ Hugging Face and GROQ secrets loaded via Hugging Face Spaces Secrets interface
@@ -242,30 +56,10 @@ if analyze_btn:
 #     "Safe": {"Urdu": "محفوظ", "French": "Sûr"}
 # }
 
-# # ✅ In-memory history
-# if "history" not in st.session_state:
-#     st.session_state.history = []
-
 # # =======================
 # # Streamlit UI
 # # =======================
 # st.set_page_config(page_title="ZeroPhish Gate", layout="wide")
-
-# st.markdown("""
-#     <style>
-#     .report-container {
-#         border: 1px solid #ddd;
-#         padding: 1rem;
-#         border-radius: 10px;
-#         background-color: #f9f9f9;
-#     }
-#     .highlight {
-#         font-weight: bold;
-#         color: #d9534f;
-#     }
-#     </style>
-# """, unsafe_allow_html=True)
-
 # st.title("🛡️ ZeroPhish Gate")
 # st.markdown("AI-powered phishing message detection and explanation.")
 
@@ -280,7 +74,6 @@ if analyze_btn:
 #     role = st.selectbox("🧑‍💼 Your Role", role_choices)
 
 # analyze_btn = st.button("🔍 Analyze with AI")
-# clear_btn = st.button("🗑️ Clear History")
 
 # # =======================
 # # Function Definitions
@@ -316,12 +109,10 @@ if analyze_btn:
 #         model="llama3-8b-8192",
 #         messages=[
 #             {"role": "system", "content": "You are a cybersecurity assistant."},
-#             {"role": "user", "content": f"Explain this suspicious message for a {role} in {language}:\n{text}"}
+#             {"role": "user", "content": f"Please explain this message in professional tone for a {role} in {language}. Do not end with questions.\n\nMessage:\n{text}"}
 #         ]
 #     )
-#     raw = response.choices[0].message.content
-#     clean = re.sub(r"Is there anything else you'd like.*", "", raw, flags=re.I).strip()
-#     return clean
+#     return response.choices[0].message.content
 
 # def translate_label(threat_type):
 #     return TRANSLATIONS.get(threat_type, {}).get(language, threat_type)
@@ -332,23 +123,31 @@ if analyze_btn:
 #         tts.save(fp.name)
 #         return fp.name
 
-# def render_history():
-#     with st.expander("🕓 View Analysis History"):
-#         for i, record in enumerate(reversed(st.session_state.history)):
-#             with st.container():
-#                 st.markdown(f"**🔢 Entry #{len(st.session_state.history) - i}**")
-#                 st.markdown(f"**📝 Input:** {record['input'][:100]}...")
-#                 st.markdown(f"**🔐 Type:** {record['threat']} | **📊 Confidence:** {record['score']}%")
-#                 st.markdown(f"**📖 Summary:** {record['summary'][:200]}...")
-#                 st.markdown("---")
+# def create_report(label, score, threat_type, explanation, text):
+#     ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+#     filename = f"Zerophish_Report_{ts}.txt"
+#     report = f"""
+# 🔍 AI Threat Detection Report
+
+# Input Message:
+# {text}
+
+# Prediction: {label}
+# Threat Type: {threat_type}
+# Confidence: {score}%
+
+# ---
+
+# 🧠 Explanation:
+# {explanation}
+# """
+#     with open(filename, "w") as f:
+#         f.write(report)
+#     return filename
 
 # # =======================
 # # Run Analysis
 # # =======================
-# if clear_btn:
-#     st.session_state.history.clear()
-#     st.success("✅ History cleared!")
-
 # if analyze_btn:
 #     combined_text = text_input
 #     if uploaded_file:
@@ -362,36 +161,237 @@ if analyze_btn:
 #         translated_threat = translate_label(threat_type)
 
 #         st.subheader("🔍 AI Threat Detection Result")
-#         st.markdown(f"<div class='report-container'>"
-#                     f"<p><span class='highlight'>Prediction:</span> {label}</p>"
-#                     f"<p><span class='highlight'>Threat Type:</span> {threat_type} ({translated_threat})</p>"
-#                     f"<p><span class='highlight'>Confidence:</span> {score}%</p>"
-#                     f"</div>", unsafe_allow_html=True)
+#         st.markdown(f"**Prediction:** {label}")
+#         st.markdown(f"**Threat Type:** {threat_type} ({translated_threat})")
+#         st.markdown(f"**Confidence:** {score}%")
 
-#         summary = ""
+#         explanation = ""
 #         if threat_type.lower() != "safe":
 #             with st.expander("🧠 Semantic Reanalysis by LLaMA"):
-#                 summary = semantic_analysis(combined_text)
-#                 st.write(summary)
+#                 explanation = semantic_analysis(combined_text)
+#                 st.write(explanation)
 
 #                 if st.button("🔊 Play Explanation as Audio"):
-#                     audio_path = text_to_speech(summary)
+#                     audio_path = text_to_speech(explanation)
 #                     with open(audio_path, "rb") as f:
 #                         st.audio(f.read(), format="audio/mp3")
 
-#         # Save history
-#         st.session_state.history.append({
-#             "input": combined_text,
-#             "threat": threat_type,
-#             "score": score,
-#             "summary": summary
-#         })
-
 #         with st.expander("📜 Glossary Help"):
 #             for term, definition in GLOSSARY.items():
 #                 st.markdown(f"**{term.capitalize()}**: {definition}")
 
-# render_history()
+#         if explanation:
+#             report_path = create_report(label, score, threat_type, explanation, combined_text)
+#             with open(report_path, "rb") as f:
+#                 b64 = base64.b64encode(f.read()).decode()
+#                 href = f'<a href="data:file/txt;base64,{b64}" download="{report_path}">📄 Download Full Report</a>'
+#                 st.markdown(href, unsafe_allow_html=True)
+
+#app v3
+import os
+import re
+import fitz  # PyMuPDF
+import tempfile
+import base64
+from datetime import datetime
+from gtts import gTTS
+import streamlit as st
+from transformers import pipeline
+from groq import Groq
+
+# ✅ Hugging Face and GROQ secrets loaded via Hugging Face Spaces Secrets interface
+
+# ⛳ Access secrets securely from environment variables
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+HF_TOKEN = os.getenv("HF_TOKEN")
+KAGGLE_USERNAME = os.getenv("KAGGLE_USERNAME")
+KAGGLE_KEY = os.getenv("KAGGLE_KEY")
+
+# ✅ Validate secrets
+if not all([GROQ_API_KEY, HF_TOKEN, KAGGLE_USERNAME, KAGGLE_KEY]):
+    st.error("❌ One or more required API keys are missing from the environment.")
+    st.stop()
+
+# ✅ Initialize Groq client
+client = Groq(api_key=GROQ_API_KEY)
+
+# ✅ Load phishing detection pipeline from Hugging Face
+phishing_pipe = pipeline(
+    "text-classification",
+    model="ealvaradob/bert-finetuned-phishing",
+    token=HF_TOKEN
+)
+
+# ✅ Language and role options
+language_choices = ["English", "Urdu", "French"]
+role_choices = ["Admin", "Procurement", "Logistics"]
+
+# ✅ Glossary terms
+GLOSSARY = {
+    "phishing": "Phishing is a scam where attackers trick you into revealing personal information.",
+    "malware": "Malicious software designed to harm or exploit systems.",
+    "spam": "Unwanted or unsolicited messages.",
+    "tone": "The emotional character of the message."
+}
+
+# ✅ Translations (demo dictionary-based)
+TRANSLATIONS = {
+    "Phishing": {"Urdu": "فشنگ", "French": "Hameçonnage"},
+    "Spam": {"Urdu": "سپیم", "French": "Courrier indésirable"},
+    "Malware": {"Urdu": "میلویئر", "French": "Logiciel malveillant"},
+    "Safe": {"Urdu": "محفوظ", "French": "Sûr"}
+}
+
+# ✅ In-memory history
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+# =======================
+# Streamlit UI
+# =======================
+st.set_page_config(page_title="ZeroPhish Gate", layout="wide")
+
+st.markdown("""
+    <style>
+    .report-container {
+        border: 1px solid #ddd;
+        padding: 1rem;
+        border-radius: 10px;
+        background-color: #f9f9f9;
+    }
+    .highlight {
+        font-weight: bold;
+        color: #d9534f;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+st.title("🛡️ ZeroPhish Gate")
+st.markdown("AI-powered phishing message detection and explanation.")
+
+# Input fields
+col1, col2 = st.columns([3, 1])
+with col1:
+    text_input = st.text_area("✉️ Paste Suspicious Message", height=200)
+    uploaded_file = st.file_uploader("📄 Upload PDF/TXT (optional)", type=["pdf", "txt"])
+
+with col2:
+    language = st.selectbox("🌐 Preferred Language", language_choices)
+    role = st.selectbox("🧑‍💼 Your Role", role_choices)
+
+analyze_btn = st.button("🔍 Analyze with AI")
+clear_btn = st.button("🗑️ Clear History")
+
+# =======================
+# Function Definitions
+# =======================
+def extract_text_from_file(file):
+    if file is None:
+        return ""
+    ext = file.name.split(".")[-1].lower()
+    if ext == "pdf":
+        doc = fitz.open(stream=file.read(), filetype="pdf")
+        return "\n".join(page.get_text() for page in doc)
+    elif ext == "txt":
+        return file.read().decode("utf-8")
+    return ""
+
+def analyze_with_huggingface(text):
+    try:
+        result = phishing_pipe(text)
+        label = result[0]['label']
+        confidence = round(result[0]['score'] * 100, 2)
+        threat_type = {
+            "PHISHING": "Phishing",
+            "SPAM": "Spam",
+            "MALWARE": "Malware",
+            "LEGITIMATE": "Safe"
+        }.get(label.upper(), "Unknown")
+        return label, confidence, threat_type
+    except Exception as e:
+        return "Error", 0, f"Error: {e}"
+
+def semantic_analysis(text):
+    response = client.chat.completions.create(
+        model="llama3-8b-8192",
+        messages=[
+            {"role": "system", "content": "You are a cybersecurity assistant."},
+            {"role": "user", "content": f"Explain this suspicious message for a {role} in {language}:\n{text}"}
+        ]
+    )
+    raw = response.choices[0].message.content
+    clean = re.sub(r"Is there anything else you'd like.*", "", raw, flags=re.I).strip()
+    return clean
+
+def translate_label(threat_type):
+    return TRANSLATIONS.get(threat_type, {}).get(language, threat_type)
+
+def text_to_speech(text):
+    tts = gTTS(text=text, lang='en')
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
+        tts.save(fp.name)
+        return fp.name
+
+def render_history():
+    with st.expander("🕓 View Analysis History"):
+        for i, record in enumerate(reversed(st.session_state.history)):
+            with st.container():
+                st.markdown(f"**🔢 Entry #{len(st.session_state.history) - i}**")
+                st.markdown(f"**📝 Input:** {record['input'][:100]}...")
+                st.markdown(f"**🔐 Type:** {record['threat']} | **📊 Confidence:** {record['score']}%")
+                st.markdown(f"**📖 Summary:** {record['summary'][:200]}...")
+                st.markdown("---")
+
+# =======================
+# Run Analysis
+# =======================
+if clear_btn:
+    st.session_state.history.clear()
+    st.success("✅ History cleared!")
+
+if analyze_btn:
+    combined_text = text_input
+    if uploaded_file:
+        extracted = extract_text_from_file(uploaded_file)
+        combined_text += "\n" + extracted
+
+    if not combined_text.strip():
+        st.warning("⚠️ Please enter some text or upload a file to analyze.")
+    else:
+        label, score, threat_type = analyze_with_huggingface(combined_text)
+        translated_threat = translate_label(threat_type)
+
+        st.subheader("🔍 AI Threat Detection Result")
+        st.markdown(f"<div class='report-container'>"
+                    f"<p><span class='highlight'>Prediction:</span> {label}</p>"
+                    f"<p><span class='highlight'>Threat Type:</span> {threat_type} ({translated_threat})</p>"
+                    f"<p><span class='highlight'>Confidence:</span> {score}%</p>"
+                    f"</div>", unsafe_allow_html=True)
+
+        summary = ""
+        if threat_type.lower() != "safe":
+            with st.expander("🧠 Semantic Reanalysis by LLaMA"):
+                summary = semantic_analysis(combined_text)
+                st.write(summary)
+
+                if st.button("🔊 Play Explanation as Audio"):
+                    audio_path = text_to_speech(summary)
+                    with open(audio_path, "rb") as f:
+                        st.audio(f.read(), format="audio/mp3")
+
+        # Save history
+        st.session_state.history.append({
+            "input": combined_text,
+            "threat": threat_type,
+            "score": score,
+            "summary": summary
+        })
+
+        with st.expander("📜 Glossary Help"):
+            for term, definition in GLOSSARY.items():
+                st.markdown(f"**{term.capitalize()}**: {definition}")
+
+render_history()
 
 #App v 3
 # app.py