Create streamlit_app.py

streamlit_app.py

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+# streamlit_app.py
+
+import os
+import streamlit as st
+from huggingface_hub import hf_hub_download
+from langchain.llms import LlamaCpp
+
+# -----------------------------
+# MODEL DOWNLOAD / LOAD
+# -----------------------------
+st.sidebar.info("Initializing model... (this may take a few minutes the first time)")
+
+os.makedirs("models", exist_ok=True)
+model_file = "BioMistral-7B.Q4_K_M.gguf"
+model_path = os.path.join("models", model_file)
+
+if not os.path.exists(model_path):
+    st.sidebar.info("Downloading GGUF model from Hugging Face Hub...")
+    model_path = hf_hub_download(
+        repo_id="MaziyarPanahi/BioMistral-7B-GGUF",
+        filename=model_file,
+        cache_dir="models"
+    )
+
+# Load the model
+llm = LlamaCpp(
+    model_path=model_path,
+    n_ctx=4096,
+    temperature=0.3,
+    max_tokens=512,
+    verbose=False
+)
+st.sidebar.success("Model loaded ✅")
+
+# -----------------------------
+# CHAT FUNCTION
+# -----------------------------
+def chat_fn(user_input):
+    try:
+        prompt = f"[INST] Answer as a mental health chatbot: {user_input} [/INST]"
+        response = llm.invoke(prompt)
+
+        # Extract text if the model returns a dict
+        if isinstance(response, dict) and "choices" in response:
+            return response["choices"][0]["text"]
+        return str(response)
+
+    except Exception as e:
+        return f"Sorry, something went wrong. ({e})"
+
+# -----------------------------
+# STREAMLIT UI
+# -----------------------------
+st.title("🧠 Mental Health Companion Bot")
+st.write("An empathetic AI assistant to support emotional well-being 💬")
+
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+user_input = st.text_input("You:", key="input")
+
+if st.button("Send") and user_input:
+    reply = chat_fn(user_input)
+    st.session_state.history.append(("You", user_input))
+    st.session_state.history.append(("Bot", reply))
+    st.session_state.input = ""  # Clear input after sending
+
+# Display chat history
+for speaker, text in st.session_state.history:
+    if speaker == "You":
+        st.markdown(f"**{speaker}:** {text}")
+    else:
+        st.markdown(f"**{speaker}:** {text}")