Update newapp.py

newapp.py

@@ -1,151 +1,151 @@
-import gradio as gr
-from openai import AzureOpenAI
-import os
-import requests
-import base64
-from dotenv import load_dotenv
-from urllib.parse import urlparse
-
-# Load environment variables from .env file
-load_dotenv()
-
-# Initialize Azure OpenAI client
-def get_openai_client():
-    try:
-        return AzureOpenAI(
-            api_key=os.getenv('AZURE_OPENAI_API_KEY').strip(),
-            api_version=os.getenv('AZURE_OPENAI_API_VERSION').strip(),
-            azure_endpoint=os.getenv('AZURE_OPENAI_ENDPOINT').strip(),
-        )
-    except Exception as e:
-        print(f"Failed to initialize OpenAI client: {e}")
-        return None
-
-
-# Expanded list of important files that usually hold project logic
-important_files = [
-    # Documentation
-    "README.md", "README.rst",
-
-    # Python backends
-    "app.py", "main.py", "server.py", "bot.py", "manage.py",
-    "config.py", "settings.py", "routes.py",
-
-    # Node.js / Express
-    "index.js", "server.js", "app.js",
-
-    # React / Next.js
-    "App.tsx", "page.tsx", "index.tsx", "_app.tsx", "_document.tsx",
-
-    # Angular / Vue
-    "main.ts", "app.component.ts", "App.vue",
-
-    # ML/AI Core
-    "model.py", "train.py", "predict.py", "inference.py",
-
-    # Setup / Environment
-    "requirements.txt", "package.json", "pyproject.toml",
-    "setup.py", "Dockerfile", ".env.example"
-]
-
-
-def fetch_repo_files(repo_url):
-    """
-    Fetch important files from a public GitHub repo.
-    """
-    try:
-        parsed = urlparse(repo_url)
-        path_parts = parsed.path.strip("/").split("/")
-        if len(path_parts) < 2:
-            return None
-
-        owner, repo = path_parts[0], path_parts[1].replace(".git", "")
-        api_url = f"https://api.github.com/repos/{owner}/{repo}/contents"
-
-        repo_content = {}
-        for file in important_files:
-            file_url = f"{api_url}/{file}"
-            resp = requests.get(file_url)
-            if resp.status_code == 200:
-                data = resp.json()
-                if data.get("encoding") == "base64":
-                    content = base64.b64decode(data["content"]).decode("utf-8", errors="ignore")
-                else:
-                    content = data.get("content", "")
-                repo_content[file] = content
-
-        return repo_content if repo_content else None
-
-    except Exception as e:
-        print(f"[ERROR] Failed to fetch repo files: {e}")
-        return None
-
-
-def generate_post(topic, tone="Professional"):
-    try:
-        client = get_openai_client()
-        if not client:
-            return "❌ OpenAI client initialization failed. Check API settings."
-
-        if not topic:
-            return "⚠️ Please provide a topic or GitHub repo link."
-
-        # Check if topic is a GitHub repo link
-        repo_content = None
-        if topic.startswith("http") and "github.com" in topic:
-            repo_content = fetch_repo_files(topic)
-
-        if repo_content:
-            combined_summary = "\n\n".join(
-                [f"--- {fname} ---\n{content}" for fname, content in repo_content.items()]
-            )
-            prompt = (
-                f"Write a {tone.lower()} LinkedIn post summarizing the following GitHub project.\n\n"
-                f"Focus on the purpose, tech stack, and innovation:\n\n{combined_summary}"
-            )
-        else:
-            prompt = f"Write a {tone.lower()} LinkedIn post about: {topic}"
-
-        messages = [
-            {"role": "system", "content": "You are a professional LinkedIn post writer."},
-            {"role": "user", "content": prompt}
-        ]
-
-        response = client.chat.completions.create(
-            model=os.getenv('AZURE_OPENAI_DEPLOYMENT'),
-            messages=messages,
-            max_tokens=1000,
-            temperature=0.9,
-            top_p=1,
-            frequency_penalty=0,
-            presence_penalty=0
-        )
-
-        return response.choices[0].message.content
-
-    except Exception as e:
-        return f"❌ Error: {str(e)}"
-
-
-# Build Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("# 🚀 LinkedIn Post Generator\nGenerate professional LinkedIn posts powered by Azure OpenAI.\n\nPaste a **topic** or a **GitHub repo link**.")
-
-    with gr.Row():
-        topic_input = gr.Textbox(label="Topic or GitHub Repo Link", placeholder="Enter project topic or GitHub repo link...")
-    
-    tone_input = gr.Dropdown(
-        ["Professional", "Casual", "Excited", "Thoughtful", "Inspirational"],
-        value="Professional",
-        label="Tone"
-    )
-
-    output = gr.Textbox(label="Generated LinkedIn Post", lines=15)
-
-    generate_btn = gr.Button("✨ Generate Post")
-    generate_btn.click(fn=generate_post, inputs=[topic_input, tone_input], outputs=output)
-
-
-# Run app
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, pwa=True)
+import gradio as gr
+from openai import AzureOpenAI
+import os
+import requests
+import base64
+from dotenv import load_dotenv
+from urllib.parse import urlparse
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Initialize Azure OpenAI client
+def get_openai_client():
+    try:
+        return AzureOpenAI(
+            api_key=os.getenv('AZURE_OPENAI_API_KEY').strip(),
+            api_version=os.getenv('AZURE_OPENAI_API_VERSION').strip(),
+            azure_endpoint=os.getenv('AZURE_OPENAI_ENDPOINT').strip(),
+        )
+    except Exception as e:
+        print(f"Failed to initialize OpenAI client: {e}")
+        return None
+
+
+# Expanded list of important files that usually hold project logic
+important_files = [
+    # Documentation
+    "README.md", "README.rst",
+
+    # Python backends
+    "app.py", "main.py", "server.py", "bot.py", "manage.py",
+    "config.py", "settings.py", "routes.py",
+
+    # Node.js / Express
+    "index.js", "server.js", "app.js",
+
+    # React / Next.js
+    "App.tsx", "page.tsx", "index.tsx", "_app.tsx", "_document.tsx",
+
+    # Angular / Vue
+    "main.ts", "app.component.ts", "App.vue",
+
+    # ML/AI Core
+    "model.py", "train.py", "predict.py", "inference.py",
+
+    # Setup / Environment
+    "requirements.txt", "package.json", "pyproject.toml",
+    "setup.py", "Dockerfile", ".env.example"
+]
+
+
+def fetch_repo_files(repo_url):
+    """
+    Fetch important files from a public GitHub repo.
+    """
+    try:
+        parsed = urlparse(repo_url)
+        path_parts = parsed.path.strip("/").split("/")
+        if len(path_parts) < 2:
+            return None
+
+        owner, repo = path_parts[0], path_parts[1].replace(".git", "")
+        api_url = f"https://api.github.com/repos/{owner}/{repo}/contents"
+
+        repo_content = {}
+        for file in important_files:
+            file_url = f"{api_url}/{file}"
+            resp = requests.get(file_url)
+            if resp.status_code == 200:
+                data = resp.json()
+                if data.get("encoding") == "base64":
+                    content = base64.b64decode(data["content"]).decode("utf-8", errors="ignore")
+                else:
+                    content = data.get("content", "")
+                repo_content[file] = content
+
+        return repo_content if repo_content else None
+
+    except Exception as e:
+        print(f"[ERROR] Failed to fetch repo files: {e}")
+        return None
+
+
+def generate_post(topic, tone="Professional"):
+    try:
+        client = get_openai_client()
+        if not client:
+            return "❌ OpenAI client initialization failed. Check API settings."
+
+        if not topic:
+            return "⚠️ Please provide a topic or GitHub repo link."
+
+        # Check if topic is a GitHub repo link
+        repo_content = None
+        if topic.startswith("http") and "github.com" in topic:
+            repo_content = fetch_repo_files(topic)
+
+        if repo_content:
+            combined_summary = "\n\n".join(
+                [f"--- {fname} ---\n{content}" for fname, content in repo_content.items()]
+            )
+            prompt = (
+                f"Write a {tone.lower()} LinkedIn post summarizing the following GitHub project.\n\n"
+                f"Focus on the purpose, tech stack, and innovation:\n\n{combined_summary}"
+            )
+        else:
+            prompt = f"Write a {tone.lower()} LinkedIn post about: {topic}"
+
+        messages = [
+            {"role": "system", "content": "You are a professional LinkedIn post writer."},
+            {"role": "user", "content": prompt}
+        ]
+
+        response = client.chat.completions.create(
+            model=os.getenv('AZURE_OPENAI_DEPLOYMENT'),
+            messages=messages,
+            max_tokens=1000,
+            temperature=0.9,
+            top_p=1,
+            frequency_penalty=0,
+            presence_penalty=0
+        )
+
+        return response.choices[0].message.content
+
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+
+# Build Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🚀 LinkedIn Post Generator\nGenerate professional LinkedIn posts powered by Azure OpenAI.\n\nPaste a **topic** or a **GitHub repo link**.")
+
+    with gr.Row():
+        topic_input = gr.Textbox(label="Topic or GitHub Repo Link", placeholder="Enter project topic or GitHub repo link...")
+    
+    tone_input = gr.Dropdown(
+        ["Professional", "Casual", "Excited", "Thoughtful", "Inspirational"],
+        value="Professional",
+        label="Tone"
+    )
+
+    output = gr.Textbox(label="Generated LinkedIn Post", lines=15)
+
+    generate_btn = gr.Button("✨ Generate Post")
+    generate_btn.click(fn=generate_post, inputs=[topic_input, tone_input], outputs=output)
+
+
+# Run app application
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, pwa=True)