Update app.py

app.py

@@ -1,64 +1,101 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import os
+import gradio as gr
+from openai import OpenAI
+from dotenv import load_dotenv
+from pypdf import PdfReader
+
+load_dotenv(override=True)
+
+api_key = os.getenv("OPENROUTER_API_KEY")
+cv_link= os.getenv("CV_LINK")
+name = os.getenv("NAME")
+
+
+if not api_key:
+    raise ValueError("OPENROUTER_API_KEY not found in .env file. Please set it.")
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+client = OpenAI(
+    base_url="https://openrouter.ai/api/v1",
+    api_key=api_key
 )
 
 
+
+summary = (
+    "I am a 3 years experienced Backend Software Engineer with a strong focus on building scalable, well-architected systems "
+    "using NestJS, GraphQL, and PostgreSQL. I have proven experience in developing high-performance APIs, "
+    "applying Domain-Driven Design (DDD), and leading AI-powered solutions such as RAG-based chatbots "
+    "integrated with vector databases like Qdrant and Pinecone. I am skilled in clean architecture, "
+    "microservices, and production-grade e-commerce systems, and I'm adept at bridging business needs with "
+    "technical implementation through both code and technical writing. I have contributed to real-time systems "
+    "using gRPC, Redis, and message queues (e.g., RabbitMQ). I'm passionate about automation, developer "
+    "tools (e.g., n8n, Copilot, Cursor), and mentoring through technical content. As an ALX alumna, I have a "
+    "hands-on mindset and a continuous learning attitude. I am also the author of a technical blog on "
+    "software engineering and AI systems: shazaali.substack.com."
+)
+
+linkedin_text = "My LinkedIn Profile: https://www.linkedin.com/in/shazaali/\n\n"
+try:
+    reader = PdfReader("linkedin.pdf")
+    for page in reader.pages:
+        linkedin_text += page.extract_text() + "\n"
+except FileNotFoundError:
+    print("Warning: 'linkedin.pdf' not found. The bot will rely only on the summary.")
+    linkedin_text = "LinkedIn profile data is not available."
+
+system_prompt = (
+    f"You are acting as {name}. You are answering questions on my personal website, "
+    f"particularly questions related to my career, background, skills, and experience. "
+    f"Your responsibility is to represent me as faithfully as possible. "
+    f"You are given a summary of my background and my LinkedIn profile to use for answering questions. "
+    f"Be professional, friendly, and engaging, as if you are talking to a potential client or future employer. "
+    f"If you don't know the answer based on the provided context, it's better to say so than to invent information. "
+    f"Always stay in character as {name}. Answer in the same language as the user's question without disclosing any private information."
+    f"\n\n## My Summary:\n{summary}\n\n## My LinkedIn Profile Text:\n{linkedin_text}\n\n## My CV:\n{cv_link}"
+)
+
+
+def chat(message, history):
+    """
+    Handles the chat logic by formatting messages and calling the OpenAI API.
+    """
+
+    formatted_history = []
+    for user_msg, assistant_msg in history:
+        formatted_history.append({"role": "user", "content": user_msg})
+        formatted_history.append({"role": "assistant", "content": assistant_msg})
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        *formatted_history,
+        {"role": "user", "content": message}
+    ]
+
+    try:
+        response = client.chat.completions.create(
+            model="openai/gpt-3.5-turbo",
+            max_tokens=300,
+            messages=messages,
+            temperature=0.7
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return "Sorry, I encountered an error while processing your request. Please try again."
+
+
+interface = gr.ChatInterface(
+    fn=chat,
+    title=f"Chat with {name}",
+    description="Ask me about my experience, skills, or projects",
+    examples=[
+        ["What are your main technical skills?"],
+        ["Tell me about your experience with AI and RAG chatbots."],
+        ["¿Hablas español?"]
+    ]
+)
+
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()