Create app.py

app.py

@@ -0,0 +1,60 @@
+import os, textwrap, torch, gradio as gr
+from transformers import (
+    pipeline,
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    BitsAndBytesConfig,
+)
+
+# ✅ 1.  Use the *Instruct* checkpoint
+MODEL_ID = os.getenv(
+    "MODEL_ID",
+    "mistralai/Mixtral-8x7B-Instruct-v0.3"       # correct model name
+)
+
+# ✅ 2.  Load in 4-bit so it fits on Hugging-Face ZeroGPU (<15 GB)
+bnb_cfg = BitsAndBytesConfig(load_in_4bit=True)
+
+tok = AutoTokenizer.from_pretrained(MODEL_ID)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    quantization_config=bnb_cfg,          # 4-bit
+    device_map="auto",
+    torch_dtype=torch.float16,            # ZeroGPU has a single T4/L4
+    trust_remote_code=True,               # required for Mixtral
+)
+
+# ✅ 3.  Use *text-generation* with an explicit prompt template
+prompt_tmpl = (
+    "Summarise the following transcript in short in 1 or 2 paragraph and point wise and don't miss any key information cover all"
+)
+
+gen = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tok,
+    max_new_tokens=256,
+    temperature=0.1,
+)
+
+MAX_CHUNK = 6_000  # ≈4 k tokens
+
+def summarize(txt):
+    parts = textwrap.wrap(txt, MAX_CHUNK, break_long_words=False)
+    partials = [
+        gen(prompt_tmpl.format(chunk=p))[0]["generated_text"].split("### Summary:")[-1].strip()
+        for p in parts
+    ]
+    return gen(prompt_tmpl.format(chunk=" ".join(partials)))[0]["generated_text"]\
+             .split("### Summary:")[-1].strip()
+
+demo = gr.Interface(
+    fn=summarize,
+    inputs=gr.Textbox(lines=20, label="Transcript"),
+    outputs="text",
+    title="Mixtral-8×7B Transcript Summariser",
+)
+
+if __name__ == "__main__":
+    demo.launch()