Update app.py

app.py

@@ -1,8 +1,24 @@
 import gradio as gr
 import requests
-import matplotlib.pyplot as plt
+import openai  # We will use OpenAI's API to generate the descriptions
 
-# Function to fetch repository details from GitHub and generate a visualization
+# OpenAI API Key (You need to set your API key here)
+openai.api_key = "your-openai-api-key-here"
+
+# Function to generate AI-based description for each contributor
+def generate_contributor_description(contributor_name, contributions):
+    prompt = f"Generate an in-depth analysis of the contribution of {contributor_name} to a GitHub repository, with {contributions} commits. Focus on the impact, type of contributions, and significance of their work."
+    
+    response = openai.Completion.create(
+        engine="text-davinci-003",  # Use the appropriate model for text generation
+        prompt=prompt,
+        max_tokens=150,
+        temperature=0.7
+    )
+    
+    return response.choices[0].text.strip()
+
+# Function to fetch repository details from GitHub and generate descriptions for contributors
 def analyze_github_repo(repo_url):
     # Extract the username and repo name from the GitHub URL
     if "github.com" not in repo_url:
@@ -41,46 +57,29 @@ def analyze_github_repo(repo_url):
                 'contributions': contributor['contributions']
             })
 
-    # Data to plot
-    metrics = {
-        'Stars': stars,
-        'Forks': forks,
-        'Open Issues': issues,
-        'Contributors': len(contributors)
-    }
-
-    # Create the bar plot
-    fig, ax = plt.subplots()
-    ax.bar(metrics.keys(), metrics.values(), color=['blue', 'green', 'red', 'orange'])
-
-    ax.set_xlabel('Metrics')
-    ax.set_ylabel('Count')
-    ax.set_title(f"GitHub Repository Analysis: {repo_url}")
+    # Prepare the text for repository details
+    result = f"Repository: {repo_url}\nStars: {stars}\nForks: {forks}\nOpen Issues: {issues}\n\nContributors' Contributions:\n"
 
-    # Save the plot as an image
-    plt.tight_layout()
-    plt.savefig("/mnt/data/github_analysis.png")
-    plt.close()
-
-    # Prepare contributor data for display
-    contributor_info = "Contributors and Their Commit Contributions:\n"
+    # Add AI-generated descriptions for each contributor
     for contributor in contributors:
-        contributor_info += f"{contributor['login']}: {contributor['contributions']} contributions\n"
-    
-    # Combine the visualization link and contributor data
-    result = f"Repository: {repo_url}\n\n{contributor_info}\nImage of Repository Analysis:\n/mnt/data/github_analysis.png"
-    
-    return result, "/mnt/data/github_analysis.png"
+        contributor_name = contributor['login']
+        contributions = contributor['contributions']
+        description = generate_contributor_description(contributor_name, contributions)
+        result += f"{contributor_name} with {contributions} contributions: {description}\n"
+
+    return result
 
 # Gradio interface for the tool
 iface = gr.Interface(
     fn=analyze_github_repo,
     inputs=gr.Textbox(label="Enter GitHub Repository URL", placeholder="https://github.com/username/repository"),
-    outputs=[gr.Textbox(label="Repository Analysis"), gr.Image(label="Repository Analysis Visualization")],
+    outputs=gr.Textbox(label="Repository Analysis"),
     title="GitHub Repository Analysis Tool",
-    description="Enter a GitHub repository URL to get basic analytics including stars, forks, issues, and contributors, along with a visual chart and contributor details."
+    description="Enter a GitHub repository URL to get an in-depth analysis of contributors' contributions using AI, including stars, forks, issues, and a detailed description of each contributor's work."
 )
 
 # Launch the app
 iface.launch()
 
+ 
+