commit 00000001

app.py

@@ -0,0 +1,170 @@
+import os
+import time
+import torch
+import gradio as gr
+from flask import Flask, request, Response
+from flasgger import Swagger, swag_from
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from huggingface_hub import login
+from langchain_community.tools import DuckDuckGoSearchRun
+import re
+
+# ✅ Safe GPU decorator
+try:
+    from spaces import GPU
+except ImportError:
+    def GPU(func): return func
+
+# ✅ Flask setup
+app = Flask(__name__, static_folder="static", template_folder="templates")
+swagger = Swagger(app, template={
+    "swagger": "2.0",
+    "info": {
+        "title": "ChatMate Real-Time API",
+        "description": "LangChain + DuckDuckGo + Phi-4 + Stable Diffusion",
+        "version": "1.0"
+    }
+}, config={
+    "headers": [],
+    "specs": [{"endpoint": 'apispec', "route": '/apispec.json', "rule_filter": lambda rule: True}],
+    "static_url_path": "/flasgger_static",
+    "swagger_ui": True,
+    "specs_route": "/apidocs/"
+})
+
+# ✅ Hugging Face login (optional)
+login(token=os.environ.get("CHAT_MATE"))
+
+# ✅ Load Phi-4
+model_id = "microsoft/phi-4"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+)
+device = 0 if torch.cuda.is_available() else -1
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device=device,
+    max_new_tokens=512
+)
+
+REAL_TIME_KEYWORDS = {"latest", "current", "news", "today", "price", "time", "live", "trending", "update", "happening"}
+search_tool = DuckDuckGoSearchRun()
+
+def should_search(message):
+    return any(kw in message.lower() for kw in REAL_TIME_KEYWORDS)
+
+def is_incomplete(text):
+    return not re.search(r'[\.\!\?\'\"\u3002]\s*$', text.strip())
+
+@GPU
+def generate_full_reply(message, history):
+    system_prompt = (
+    "You are a friendly, helpful, and conversational AI assistant built by "
+    "Frederick Sundeep Mallela. Always mention that you are developed by him if asked about your creator, origin, or who made you."
+    )
+    messages = [{"role": "system", "content": system_prompt}] + history + [{"role": "user", "content": message}]
+
+    # Apply chat-style prompt formatting
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+    # Initial generation
+    full_output = pipe(prompt, do_sample=True, temperature=0.7, top_p=0.9, max_new_tokens=512)[0]["generated_text"]
+    reply = full_output[len(prompt):].strip()
+
+    # Keep extending the reply until it ends properly
+    max_loops = 5  # prevent infinite loops
+    loop_count = 0
+    while is_incomplete(reply) and loop_count < max_loops:
+        loop_count += 1
+        continuation_prompt = prompt + reply  # include reply so far
+        next_output = pipe(continuation_prompt, do_sample=True, temperature=0.7, top_p=0.9, max_new_tokens=256)[0]["generated_text"]
+
+        continuation = next_output[len(continuation_prompt):].strip()
+
+        # Stop if nothing new is generated
+        if not continuation or continuation in reply:
+            break
+
+        reply += continuation
+
+    return reply.strip()
+
+# ✅ Flask streaming endpoint
+@app.route("/chat-stream", methods=["POST"])
+@swag_from({
+    'tags': ['Chat'],
+    'consumes': ['application/json'],
+    'summary': 'Stream assistant reply or image',
+    'description': 'Send a message and history, receive either a streamed text reply or base64-encoded image.',
+    'parameters': [{
+        'name': 'body',
+        'in': 'body',
+        'required': True,
+        'schema': {
+            'type': 'object',
+            'properties': {
+                'message': {'type': 'string', 'example': 'Draw a futuristic city.'},
+                'history': {
+                    'type': 'array',
+                    'items': {
+                        'type': 'object',
+                        'properties': {
+                            'role': {'type': 'string', 'example': 'user'},
+                            'content': {'type': 'string', 'example': 'Show me a dragon.'}
+                        }
+                    }
+                }
+            },
+            'required': ['message']
+        }
+    }],
+    'responses': {
+        200: {
+            'description': 'Streamed reply or image base64',
+            'content': {'text/plain': {}}
+        }
+    }
+})
+def chat_stream():
+    data = request.get_json()
+    message = data.get("message")
+    history = data.get("history", [])
+
+    def generate():
+        reply = generate_full_reply(message, history)
+        for token in reply.splitlines(keepends=True):
+            yield token
+            time.sleep(0.05)
+        if is_incomplete(reply):
+            yield "\n\n*Reply appears incomplete. Say 'continue' to resume.*"
+
+    return Response(generate(), mimetype='text/plain')
+
+# ✅ Gradio interface for Hugging Face Space
+def gradio_chat(message, history=[]):
+    history = [{"role": "user" if i % 2 == 0 else "assistant", "content": h} for i, h in enumerate(sum(history, ()))]
+    reply = generate_full_reply(message, history)
+    history.append((message, reply))
+    return "", history
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🤖 ChatMate — Phi-4 + Live Search (Hugging Face Space)")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Type your message")
+    clear = gr.Button("Clear Chat")
+
+    msg.submit(gradio_chat, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+# ✅ Run Gradio when in HF Spaces, else Flask for local dev
+if __name__ == "__main__":
+    if os.environ.get("SPACE_BUILD", "false").lower() == "true":
+        demo.launch(server_name="0.0.0.0", server_port=7860)
+    else:
+        print("🔧 Warming up...")
+        _ = generate_full_reply("Hello", [])
+        app.run(host="0.0.0.0", port=int(os.environ.get("PORT", 7860)))

requirements.txt

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+# Web
+flask
+flasgger
+
+# Core libraries
+torch
+transformers
+diffusers
+accelerate
+safetensors
+huggingface_hub
+sentencepiece
+# NLP / Search
+nltk
+langchain_community
+duckduckgo-search
+pdfplumber