Create app.py

app.py

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+import gradio as gr
+import faiss
+import numpy as np
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+import google.generativeai as genai
+import re
+import os
+
+# Load data and FAISS index
+def load_data_and_index():
+    docs_df = pd.read_pickle("docs_with_embeddings (1).pkl")  # Adjust path for HF Spaces
+    embeddings = np.array(docs_df['embeddings'].tolist(), dtype=np.float32)
+    dimension = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dimension)
+    index.add(embeddings)
+    return docs_df, index
+
+docs_df, index = load_data_and_index()
+
+# Load SentenceTransformer
+minilm = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Configure Gemini API using Hugging Face Secrets
+GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
+if not GEMINI_API_KEY:
+    raise ValueError("Gemini API key not found. Please set it in Hugging Face Spaces secrets.")
+genai.configure(api_key=GEMINI_API_KEY)
+model = genai.GenerativeModel('gemini-2.0-flash')
+
+# Preprocess text function
+def preprocess_text(text):
+    text = text.lower()
+    text = text.replace('\n', ' ').replace('\t', ' ')
+    text = re.sub(r'[^\w\s.,;:>-]', ' ', text)
+    text = ' '.join(text.split()).strip()
+    return text
+
+# Retrieve documents
+def retrieve_docs(query, k=5):
+    query_embedding = minilm.encode([query], show_progress_bar=False)[0].astype(np.float32)
+    distances, indices = index.search(np.array([query_embedding]), k)
+    retrieved_docs = docs_df.iloc[indices[0]][['label', 'text', 'source']]
+    retrieved_docs['distance'] = distances[0]
+    return retrieved_docs
+
+# RAG pipeline integrated into respond function
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,  # Keeping top_p as an input, though Gemini doesn’t use it directly
+):
+    # Preprocess the user message
+    preprocessed_query = preprocess_text(message)
+    
+    # Retrieve relevant documents
+    retrieved_docs = retrieve_docs(preprocessed_query, k=5)
+    context = "\n".join(retrieved_docs['text'].tolist())
+    
+    # Construct the prompt with system message, history, and RAG context
+    prompt = f"{system_message}\n\n"
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            prompt += f"User: {user_msg}\n"
+        if assistant_msg:
+            prompt += f"Assistant: {assistant_msg}\n"
+    prompt += (
+        f"Query: {message}\n"
+        f"Relevant Context: {context}\n"
+        f"Generate a short, concise, and to-the-point response to the query based only on the provided context."
+    )
+    
+    # Generate response with Gemini
+    response = model.generate_content(
+        prompt,
+        generation_config=genai.types.GenerationConfig(
+            max_output_tokens=max_tokens,
+            temperature=temperature
+        )
+    )
+    answer = response.text.strip()
+    if not answer.endswith('.'):
+        last_period = answer.rfind('.')
+        if last_period != -1:
+            answer = answer[:last_period + 1]
+        else:
+            answer += "."
+    
+    # Yield the full response (no streaming, as Gemini API doesn’t support it here)
+    yield answer
+
+# Gradio Chat Interface
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(
+            value="You are a medical AI assistant diagnosing patients based on their query, using relevant context from past records of other patients.",
+            label="System message"
+        ),
+        gr.Slider(minimum=1, maximum=2048, value=150, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.75, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",  # Included but not used by Gemini
+        ),
+    ],
+    title="🏥 Medical Chat Assistant",
+    description="A chat-based medical assistant that diagnoses patient queries using AI and past records."
+)
+
+if __name__ == "__main__":
+    demo.launch()