Create app.py

app.py

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+import streamlit as st
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# Initialize the model via Hugging Face Inference API
+MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
+client = InferenceClient(MODEL_NAME)
+
+# Streamlit UI for displaying project information
+st.title("Project Guidance AI Chatbot")
+st.subheader("This chatbot is powered by DeepSeek-R1-Distill-Qwen-7B")
+
+# Display the roadmap and rules from files
+def load_file(filename):
+    try:
+        with open(filename, "r", encoding="utf-8") as file:
+            return file.read()
+    except FileNotFoundError:
+        return f"⚠️ Error: {filename} not found!"
+
+roadmap_content = load_file("roadmap.txt")
+rules_content = load_file("rules.txt")
+
+st.write("### Project Roadmap")
+st.text(roadmap_content)
+
+st.write("### Project Rules")
+st.text(rules_content)
+
+# Gradio component for model interaction (as part of Streamlit)
+def generate_response(user_input):
+    system_message = f"Project Roadmap:\n{roadmap_content}\n\nProject Rules:\n{rules_content}"
+    messages = [{"role": "system", "content": system_message},
+                {"role": "user", "content": user_input}]
+    
+    response = client.chat_completion(messages)
+    return response.choices[0].delta.content
+
+gr.Interface(fn=generate_response, inputs="text", outputs="text").launch(share=True)