Please address the comments from this code review:

## Overall Comments
- You should configure CORS (e.g., via flask-cors) on the Flask API so the React frontend can reliably call `/analyze/*` endpoints without cross‐origin errors.
- Consider handling missing NLTK data and the GOOGLE_API_KEY at startup (or auto–download required corpora) to avoid runtime failures when the environment isn’t preconfigured.

## Individual Comments

### Comment 1
<location> `text_message_analyzer/app.py:36` </location>
<code_context>
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=8080)
+if __name__ == '__main__':
+    app.run(debug=True)
</code_context>

<issue_to_address>
**🚨 issue (security):** Enabling debug mode in production can expose sensitive information.

Ensure debug mode is disabled in production, ideally by controlling it with an environment variable.
</issue_to_address>

### Comment 2
<location> `social_media_analyzer/fake_news_detector.py:29-32` </location>
<code_context>

     score = 0.0
     indicators_found = []
+    named_entities = {
+        "organizations": [],
+        "persons": [],
+    }

     # 1. Check against known fake news domains
</code_context>

<issue_to_address>
**issue:** Named entity extraction does not handle cases where NLTK data is missing.

Catch NLTK exceptions and provide a user-friendly error message or guidance on downloading missing data.
</issue_to_address>

### Comment 3
<location> `text_message_analyzer/app.py:36` </location>
<code_context>
    app.run(debug=True)
</code_context>

<issue_to_address>
**security (python.flask.security.audit.debug-enabled):** Detected Flask app with debug=True. Do not deploy to production with this flag enabled as it will leak sensitive information. Instead, consider using Flask configuration variables or setting 'debug' using system environment variables.

*Source: opengrep*
</issue_to_address>

### Comment 4
<location> `social_media_analyzer/fake_news_detector.py:23` </location>
<code_context>
def analyze_url_for_fake_news(url):
    """
    Analyzes a URL for indicators of fake news.

    NOTE: This function requires the following NLTK data to be downloaded:
    - 'punkt'
    - 'averaged_perceptron_tagger'
    - 'maxent_ne_chunker'
    - 'words'
    """
    if not url.startswith(('http://', 'https://')):
        url = 'http://' + url

    domain = urlparse(url).netloc.lower()

    score = 0.0
    indicators_found = []
    named_entities = {
        "organizations": [],
        "persons": [],
    }

    # 1. Check against known fake news domains
    if domain in FAKE_NEWS_DOMAINS:
        score += HEURISTIC_WEIGHTS.get("KNOWN_FAKE_NEWS_DOMAIN", 5.0)
        indicators_found.append(f"Domain '{domain}' is a known source of fake news.")
        return {
            "url": url,
            "score": round(score, 2),
            "indicators_found": indicators_found
        }

    # 2. Fetch and analyze content
    try:
        headers = {'User-Agent': 'Mozilla/5.0'}
        request = urllib.request.Request(url, headers=headers)
        with urllib.request.urlopen(request, timeout=10) as response:
            if response.status == 200:
                html_content = response.read().decode('utf-8', errors='ignore')
                text_content = re.sub(r'<[^>]+>', '', html_content).lower()

                # 3. Analyze text for sensationalist keywords
                for keyword in SENSATIONALIST_KEYWORDS:
                    if keyword in text_content:
                        score += HEURISTIC_WEIGHTS.get("SENSATIONALIST_KEYWORD", 1.0)
                        indicators_found.append(f"Found sensationalist keyword: '{keyword}'")

                # 4. Analyze text for clickbait patterns
                for pattern in CLICKBAIT_PATTERNS:
                    if re.search(pattern, text_content, re.IGNORECASE):
                        score += HEURISTIC_WEIGHTS.get("CLICKBAIT_PATTERN", 1.5)
                        indicators_found.append(f"Found clickbait pattern: '{pattern}'")

                # 5. Named Entity Recognition
                tokens = nltk.word_tokenize(text_content)
                tagged = nltk.pos_tag(tokens)
                entities = nltk.ne_chunk(tagged)

                for entity in entities:
                    if isinstance(entity, nltk.Tree):
                        entity_text = " ".join([word for word, tag in entity.leaves()])
                        if entity.label() == 'ORGANIZATION':
                            if entity_text not in named_entities["organizations"]:
                                named_entities["organizations"].append(entity_text)
                        elif entity.label() == 'PERSON':
                            if entity_text not in named_entities["persons"]:
                                named_entities["persons"].append(entity_text)

            else:
                return {"error": f"Failed to fetch URL: HTTP status code {response.status}"}
    except Exception as e:
        return {"error": f"An error occurred: {e}"}

    return {
        "url": url,
        "score": round(score, 2),
        "indicators_found": indicators_found,
        "named_entities": named_entities
    }

</code_context>

<issue_to_address>
**issue (code-quality):** We've found these issues:

- Use f-string instead of string concatenation ([`use-fstring-for-concatenation`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/use-fstring-for-concatenation/))
- Swap if/else branches ([`swap-if-else-branches`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/swap-if-else-branches/))
- Remove unnecessary else after guard condition ([`remove-unnecessary-else`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/remove-unnecessary-else/))
</issue_to_address>

### Comment 5
<location> `social_media_analyzer/test_scam_detector.py:10-15` </location>
<code_context>
    def test_sentiment_analysis(self):
        # Test case for negative sentiment
        text_negative = "This is a terrible, awful, no good, very bad message."
        result_negative = analyze_text_for_scams(text_negative)
        self.assertIn("Strong negative sentiment detected in text.", [indicator for indicator in result_negative["indicators_found"]])

        # Test case for positive sentiment
        text_positive = "This is a wonderful, amazing, great message."
        result_positive = analyze_text_for_scams(text_positive)
        self.assertNotIn("Strong negative sentiment detected in text.", [indicator for indicator in result_positive["indicators_found"]])

</code_context>

<issue_to_address>
**issue (code-quality):** Replace identity comprehension with call to collection constructor [×2] ([`identity-comprehension`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/identity-comprehension/))

<br/><details><summary>Explanation</summary>Convert list/set/tuple comprehensions that do not change the input elements into.

#### Before

```python
# List comprehensions
[item for item in coll]
[item for item in friends.names()]

# Dict comprehensions
{k: v for k, v in coll}
{k: v for k, v in coll.items()}  # Only if we know coll is a `dict`

# Unneeded call to `.items()`
dict(coll.items())  # Only if we know coll is a `dict`

# Set comprehensions
{item for item in coll}
```

#### After

```python
# List comprehensions
list(iter(coll))
list(iter(friends.names()))

# Dict comprehensions
dict(coll)
dict(coll)

# Unneeded call to `.items()`
dict(coll)

# Set comprehensions
set(coll)
```

All these comprehensions are just creating a copy of the original collection.
They can all be simplified by simply constructing a new collection directly. The
resulting code is easier to read and shows the intent more clearly.
Convert list/set/tuple comprehensions that do not change the input elements into.

#### Before

```python
# List comprehensions
[item for item in coll]
[item for item in friends.names()]

# Dict comprehensions
{k: v for k, v in coll}
{k: v for k, v in coll.items()}  # Only if we know coll is a `dict`

# Unneeded call to `.items()`
dict(coll.items())  # Only if we know coll is a `dict`

# Set comprehensions
{item for item in coll}
```

#### After

```python
# List comprehensions
list(iter(coll))
list(iter(friends.names()))

# Dict comprehensions
dict(coll)
dict(coll)

# Unneeded call to `.items()`
dict(coll)

# Set comprehensions
set(coll)
```

All these comprehensions are just creating a copy of the original collection.
They can all be simplified by simply constructing a new collection directly. The
resulting code is easier to read and shows the intent more clearly.</details>
</issue_to_address>

### Comment 6
<location> `social_media_analyzer/test_scam_detector.py:19-26` </location>
<code_context>
    def test_keyword_matching(self):
        # Test case for urgency keyword
        text_urgency = "URGENT: Your account has been compromised."
        result_urgency = analyze_text_for_scams(text_urgency)
        self.assertIn("Presence of 'Urgency' keyword: 'urgent'", [indicator for indicator in result_urgency["indicators_found"]])

        # Test case for stemming
        text_stemming = "I need you to verify your account immediately."
        result_stemming = analyze_text_for_scams(text_stemming)
        self.assertIn("Presence of 'Sensitive Info' keyword: 'verify your account'", [indicator for indicator in result_stemming["indicators_found"]])

</code_context>

<issue_to_address>
**issue (code-quality):** We've found these issues:

- Extract duplicate code into method ([`extract-duplicate-method`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/extract-duplicate-method/))
- Replace identity comprehension with call to collection constructor [×2] ([`identity-comprehension`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/identity-comprehension/))

<br/><details><summary>Explanation</summary>
Convert list/set/tuple comprehensions that do not change the input elements into.

#### Before

```python
# List comprehensions
[item for item in coll]
[item for item in friends.names()]

# Dict comprehensions
{k: v for k, v in coll}
{k: v for k, v in coll.items()}  # Only if we know coll is a `dict`

# Unneeded call to `.items()`
dict(coll.items())  # Only if we know coll is a `dict`

# Set comprehensions
{item for item in coll}
```

#### After

```python
# List comprehensions
list(iter(coll))
list(iter(friends.names()))

# Dict comprehensions
dict(coll)
dict(coll)

# Unneeded call to `.items()`
dict(coll)

# Set comprehensions
set(coll)
```

All these comprehensions are just creating a copy of the original collection.
They can all be simplified by simply constructing a new collection directly. The
resulting code is easier to read and shows the intent more clearly.
Convert list/set/tuple comprehensions that do not change the input elements into.

#### Before

```python
# List comprehensions
[item for item in coll]
[item for item in friends.names()]

# Dict comprehensions
{k: v for k, v in coll}
{k: v for k, v in coll.items()}  # Only if we know coll is a `dict`

# Unneeded call to `.items()`
dict(coll.items())  # Only if we know coll is a `dict`

# Set comprehensions
{item for item in coll}
```

#### After

```python
# List comprehensions
list(iter(coll))
list(iter(friends.names()))

# Dict comprehensions
dict(coll)
dict(coll)

# Unneeded call to `.items()`
dict(coll)

# Set comprehensions
set(coll)
```

All these comprehensions are just creating a copy of the original collection.
They can all be simplified by simply constructing a new collection directly. The
resulting code is easier to read and shows the intent more clearly.</details>
</issue_to_address>