Update app.py

app.py

@@ -1,64 +1,109 @@
 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)",
-        ),
-    ],
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+import torch
+
+# Load model
+model_id = "ibm-granite/granite-3b-code-instruct"  # Replace with actual granite model if different
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.float16
 )
 
+# Load sentiment analysis model
+sentiment_analyzer = pipeline("sentiment-analysis")
+
+# Simulated citizen profiles
+user_profiles = {
+    "1001": {"location": "Hyderabad", "issues": ["traffic", "air pollution"]},
+    "1002": {"location": "Delhi", "issues": ["waste management", "noise"]},
+}
+
+# Store submitted feedback during session
+submitted_data = []
+
+# Chat Function (ChatGPT-style)
+def chat_fn(message, history):
+    full_prompt = tokenizer.apply_chat_template(
+        [{"role": "user", "content": message}],
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=200)
+    reply = tokenizer.decode(outputs[0], skip_special_tokens=True).split("assistant")[-1].strip()
+    return reply
+
+# Sentiment Analysis
+def analyze_sentiment(text):
+    result = sentiment_analyzer(text)[0]
+    return f"{result['label']} ({result['score']*100:.2f}%)"
+
+# Live Feedback → Dashboard
+def collect_and_plot_feedback(comment, category):
+    sentiment = sentiment_analyzer(comment)[0]["label"]
+    submitted_data.append({"Category": category, "Sentiment": sentiment})
+
+    df = pd.DataFrame(submitted_data)
+    summary = df.groupby(['Category', 'Sentiment']).size().unstack(fill_value=0)
+
+    fig, ax = plt.subplots(figsize=(8, 5))
+    summary.plot(kind='bar', stacked=True, ax=ax, colormap="Set2")
+    plt.title("Live Citizen Sentiment by Category")
+    plt.ylabel("Count")
+    plt.tight_layout()
+    return f"Recorded sentiment: {sentiment}", fig
+
+# Personalized Contextual Assistant
+def personalized_response(user_id, query):
+    profile = user_profiles.get(user_id)
+    if not profile:
+        return "User profile not found. Please check your user ID."
+    context = f"User from {profile['location']} concerned with: {', '.join(profile['issues'])}. Question: {query}"
+    input_tokens = tokenizer(context, return_tensors="pt").to(model.device)
+    output = model.generate(**input_tokens, max_new_tokens=150)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Build Gradio App
+with gr.Blocks(title="Citizen AI – Intelligent Citizen Engagement Platform") as demo:
+    gr.Markdown("## 🧠 Citizen AI – Intelligent Citizen Engagement Platform")
+
+    with gr.Tab("🤖 Chat Assistant"):
+        gr.ChatInterface(
+            fn=chat_fn,
+            title="🧠 Ask Citizen AI",
+            theme="soft",
+            chatbot=gr.Chatbot(label="Citizen Chat"),
+            textbox=gr.Textbox(placeholder="Type your question here...", show_label=False),
+            retry_btn="🔁 Retry",
+            clear_btn="🗑️ Clear",
+            submit_btn="➤ Send"
+        )
+
+    with gr.Tab("📊 Sentiment Analysis"):
+        sentiment_input = gr.Textbox(label="Enter citizen comment")
+        sentiment_output = gr.Textbox(label="Sentiment Result")
+        analyze_btn = gr.Button("Analyze")
+        analyze_btn.click(analyze_sentiment, inputs=sentiment_input, outputs=sentiment_output)
+
+    with gr.Tab("📈 Live Dashboard"):
+        gr.Markdown("### 📬 Submit Feedback and Watch Sentiment Grow Live")
+        comment_input = gr.Textbox(label="Citizen Feedback")
+        category_input = gr.Dropdown(choices=["Healthcare", "Sanitation", "Transport", "Education"], label="Category")
+        submit_button = gr.Button("Submit Feedback")
+        sentiment_display = gr.Textbox(label="Detected Sentiment")
+        live_chart = gr.Plot(label="Live Sentiment Chart")
+        submit_button.click(collect_and_plot_feedback, inputs=[comment_input, category_input], outputs=[sentiment_display, live_chart])
+
+    with gr.Tab("🧬 Personalized AI Response"):
+        uid_input = gr.Textbox(label="User ID (e.g., 1001)")
+        query_input = gr.Textbox(label="Your query")
+        response_output = gr.Textbox(label="AI Response")
+        personal_btn = gr.Button("Generate Personalized Response")
+        personal_btn.click(personalized_response, inputs=[uid_input, query_input], outputs=response_output)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch(share=True)