Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+from sentence_transformers import SentenceTransformer
+import torch
+import re
+
+# Initialize models and client
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
+
+# Load Medicaid/health info
+with open("medicaid_info.txt", "r", encoding="utf-8") as file:
+    health_info = file.read()
+
+# Load clinic info
+with open("info.txt", "r", encoding="utf-8") as file:
+    clinic_data = file.read().lower()
+
+# Preprocess info into chunks
+def preprocess_chunks(text):
+    chunks = [chunk.strip() for chunk in text.split('.') if chunk.strip()]
+    return chunks
+
+health_chunks = preprocess_chunks(health_info)
+health_embeddings = embedding_model.encode(health_chunks, convert_to_tensor=True)
+
+# Semantic search
+def get_relevant_chunks(query, embeddings, text_chunks):
+    query_embedding = embedding_model.encode(query, convert_to_tensor=True)
+    query_embedding = query_embedding / query_embedding.norm()
+    embeddings = embeddings / embeddings.norm(dim=1, keepdim=True)
+    similarities = torch.matmul(embeddings, query_embedding)
+    top_indices = torch.topk(similarities, k=3).indices
+    return [text_chunks[i] for i in top_indices]
+
+# Clinic finder
+def find_clinic_by_county(county):
+    county = county.lower()
+    sections = clinic_data.split("###")
+    for section in sections:
+        if county in section:
+            lines = section.strip().split("\n")
+            if lines and county in lines[0].lower():
+                lines = lines[1:]
+            return "\n".join(lines).strip()
+    return "⚠️ Sorry, I couldn’t find clinics for that county. Check spelling or try a nearby county."
+
+# Chatbot logic
+def respond(message, history, name, focus_area):
+    focus = focus_area[0] if focus_area else "general help"
+    top_chunks = get_relevant_chunks(message, health_embeddings, health_chunks)
+    context = "\n".join(top_chunks)
+
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                f"You are a friendly and supportive health advisor chatbot helping Washington residents, especially low-income individuals. "
+                f"You're chatting with {name}. Speak clearly and kindly, and keep responses under 100 words. Focus on {focus}."
+            )
+        },
+        {
+            "role": "user",
+            "content": f"Context:\n{context}\n\nQuestion: {message}"
+        }
+    ]
+    
+    response = client.chat_completion(messages, max_tokens=120)
+    return response['choices'][0]['message']['content'].strip()
+
+# Interface styling
+title = "# 🩺 HealthHelpBot"
+description = "### Helping Washington residents find care, understand Medicaid, and locate free clinics."
+
+with gr.Blocks(theme="gstaff/soft-blue") as demo:
+    with gr.Row():
+        gr.Markdown(title)
+    with gr.Row():
+        gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Image("healthbot_avatar.png", width=250, show_label=False)
+            user_name = gr.Textbox(label="👤 Your Name", placeholder="e.g. Maria")
+            focus_area = gr.CheckboxGroup(["Medicaid", "Finding a clinic", "Insurance help"], label="What do you need help with?")
+        with gr.Column(scale=2):
+            gr.ChatInterface(fn=respond, additional_inputs=[user_name, focus_area], type="messages")
+
+demo.launch()