Update app.py

app.py

@@ -1,19 +1,52 @@
 import streamlit as st
 import requests
 from bs4 import BeautifulSoup
+import pandas as pd
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
+from datasets import load_dataset, Dataset
 
-def fetch_page_title(url):
-    """
-    Fetches the title of the given URL.
+# OSINT functions
+def get_github_stars_forks(owner, repo):
+    url = f"https://api.github.com/repos/{owner}/{repo}"
+    response = requests.get(url)
+    data = response.json()
+    return data['stargazers_count'], data['forks_count']
+
+def get_github_issues(owner, repo):
+    url = f"https://api.github.com/repos/{owner}/{repo}/issues"
+    response = requests.get(url)
+    issues = response.json()
+    return len(issues)
+
+def get_github_pull_requests(owner, repo):
+    url = f"https://api.github.com/repos/{owner}/{repo}/pulls"
+    response = requests.get(url)
+    pulls = response.json()
+    return len(pulls)
 
-    Args:
-        url (str): The URL of the webpage.
+def get_github_license(owner, repo):
+    url = f"https://api.github.com/repos/{owner}/{repo}/license"
+    response = requests.get(url)
+    data = response.json()
+    return data['license']['name']
 
-    Returns:
-        str: The title of the webpage or an error message.
-    """
+def get_last_commit(owner, repo):
+    url = f"https://api.github.com/repos/{owner}/{repo}/commits"
+    response = requests.get(url)
+    commits = response.json()
+    return commits[0]['commit']['committer']['date']
+
+def get_github_workflow_status(owner, repo):
+    url = f"https://api.github.com/repos/{owner}/{repo}/actions/runs"
+    response = requests.get(url)
+    runs = response.json()
+    return runs['workflow_runs'][0]['status'] if runs['workflow_runs'] else "No workflows found"
+
+# Function to fetch page title from a URL
+def fetch_page_title(url):
     try:
         response = requests.get(url)
+        st.write(f"Fetching URL: {url} - Status Code: {response.status_code}")
         if response.status_code == 200:
             soup = BeautifulSoup(response.text, 'html.parser')
             title = soup.title.string if soup.title else 'No title found'
@@ -23,17 +56,58 @@ def fetch_page_title(url):
     except Exception as e:
         return f"An error occurred: {e}"
 
+# Main Streamlit app
 def main():
-    """
-    Main function to run the Streamlit application.
-    """
     st.title("OSINT Tool")
-    st.write("Enter a URL to fetch its title:")
+    
+    st.write("### GitHub Repository OSINT Analysis")
+    st.write("Enter the GitHub repository owner and name:")
 
-    url = st.text_input("URL")
+    owner = st.text_input("Repository Owner")
+    repo = st.text_input("Repository Name")
+
+    if owner and repo:
+        stars, forks = get_github_stars_forks(owner, repo)
+        open_issues = get_github_issues(owner, repo)
+        open_pulls = get_github_pull_requests(owner, repo)
+        license_type = get_github_license(owner, repo)
+        last_commit = get_last_commit(owner, repo)
+        workflow_status = get_github_workflow_status(owner, repo)
+
+        st.write(f"Stars: {stars}, Forks: {forks}")
+        st.write(f"Open Issues: {open_issues}, Open Pull Requests: {open_pulls}")
+        st.write(f"License: {license_type}")
+        st.write(f"Last Commit: {last_commit}")
+        st.write(f"Workflow Status: {workflow_status}")
+
+    st.write("### URL Title Fetcher")
+    url = st.text_input("Enter a URL to fetch its title:")
     if url:
         title = fetch_page_title(url)
         st.write(f"Title: {title}")
+    
+    st.write("### Dataset Upload & Model Fine-Tuning")
+    dataset_file = st.file_uploader("Upload a CSV file for fine-tuning", type=["csv"])
+    if dataset_file:
+        df = pd.read_csv(dataset_file)
+        st.dataframe(df.head())
+
+    st.write("Select a model for fine-tuning:")
+    model_name = st.selectbox("Model", ["bert-base-uncased", "distilbert-base-uncased"])
+    if st.button("Fine-tune Model"):
+        if dataset_file:
+            dataset = Dataset.from_pandas(df)
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+            def tokenize_function(examples):
+                return tokenizer(examples['text'], padding="max_length", truncation=True)
+
+            tokenized_datasets = dataset.map(tokenize_function, batched=True)
+            training_args = TrainingArguments(output_dir="./results", num_train_epochs=1, per_device_train_batch_size=8)
+            trainer = Trainer(model=model, args=training_args, train_dataset=tokenized_datasets)
+            trainer.train()
+            st.write("Model fine-tuned successfully!")
 
 if __name__ == "__main__":
     main()