Update app.py

app.py

@@ -1,12 +1,13 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-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")
+data = '''
+SARATH CHANDRA BANDREDDI **Professional Summary** Intern-level Python Developer skilled in AI, Django, and web development. Proficient in Python and AI, dedicated to continuous learning. Keen to contribute to software solutions and collaborate in a team environment. **Education** B.Tech in Computer Science (AI & ML), Vasireddy Venkatadri Institute of Technology, India (2021–2025), GPA: 8.60 **Tech Stack** - **Languages**: Python, Java, JavaScript, C, R, Shell - **Frontend**: HTML, CSS, Jinja - **Libraries**: TensorFlow, Keras, LlamaIndex, OpenCV, Sklearn, Numpy, Pandas, Django - **Platforms**: Kaggle, Google-Colabs, Git, GitHub, AWS, Figma - **Databases**: SQL, Oracle, MySQL **Projects** - **Face Recognition** (VGGFace | TensorFlow, Keras, OpenCV, Django) Developed a face recognition app achieving 97.86% accuracy on a 31-class dataset. Launched through Django on an online server. - **SCRAM** (Gemini-pro LLM, Google API, TensorFlow, Django) Built a Django-based college resource management system with LLM chatbot, face recognition, and 2-FS authentication. - **Document & Data Query AI Agent** (Llama3, Ollama, LlamaIndex) Created an AI agent for document-based Q&A and CSV visualization, integrating NLP and data retrieval. **Certifications** Python: Kaggle, SoloLearn, HackerRank, GUVI ML: Coursera, Kaggle, Microsoft, IBM Deep Learning: NPTEL, Kaggle, IBM Django: Microsoft AWS: AWS Academy Cloud Foundations, Cloud Architecting NPTEL: Java (ELITE), Data Science (ELITE + SILVER) **Achievements** 2nd Place in Python Hackathon (15 colleges, RVR&JC) **Find Me Online** LinkedIn, GitHub, Kaggle, HackerRank, CodeChef, LeetCode, Portfolio
+'''
 
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# Chatbot response function with integrated system message
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -15,18 +16,27 @@ def respond(
     temperature,
     top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
+    # System message defining assistant behavior
+    system_message = {
+        "role": "system",
+        "content": f"Act and chat as a professional fresher seeking a job and your name is SARATH. Here is about you: {data}. You should answer questions based on this information only."
+    }
+
+    messages = [system_message]
 
+    # Adding conversation history
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
 
+    # Adding the current user input
     messages.append({"role": "user", "content": message})
 
     response = ""
 
+    # Streaming the response from the API
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -35,30 +45,85 @@ def respond(
         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
-"""
+# Gradio interface with additional sliders for control
 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)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
 )
 
-
 if __name__ == "__main__":
     demo.launch()
+
+# import gradio as gr
+# from huggingface_hub import InferenceClient
+
+# """
+# 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)",
+#         ),
+#     ],
+# )
+
+
+# if __name__ == "__main__":
+#     demo.launch()